International Journal of Electrical Power & Energy Systems最新文献

{"title":"A secure and efficient data aggregation scheme for cloud–edge collaborative smart meters","authors":"","doi":"10.1016/j.ijepes.2024.110270","DOIUrl":"10.1016/j.ijepes.2024.110270","url":null,"abstract":"<div><div>Smart meters are part of the Advanced Measurement Infrastructure (AMI) system in the smart grid. It facilitates data transfer between consumers and electricity suppliers (ES). However, the mass deployment of smart meters (SM) brings heavy overhead to grid operation and poses serious privacy threats. To this end, this paper proposes a secure and efficient data aggregation scheme of cloud–edge collaboration smart meters. At first, we standardize the users’ historical electricity load features and use the improved K-Means clustering algorithm to calculate the Euclidean distance between feature vectors to obtain the classification results of users’ load features. On this basis, ES generates relevant parameters to encrypt meter data and protect users’ data privacy based on classification results. The aggregator (Ag) performs the data aggregation, generates the overall signature using the Schnorr aggregation signature method, and sends it to the cloud server (CS). The ES queries the CS to obtain data and parses it to realize the customer billing service. Meanwhile, this paper executes a series of experiments, and the results show that the proposed scheme exhibits significant advantages in privacy protection and system operation efficiency.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of distribution network fault using multiresolution S-transform and modified convolution neural network","authors":"","doi":"10.1016/j.ijepes.2024.110294","DOIUrl":"10.1016/j.ijepes.2024.110294","url":null,"abstract":"<div><div>This study presents a new modified convolution neural network (MCNN) based on a three-stage fault diagnosis framework. An improved multiresolution S-transform (MST) model is proposed initially to calculate the points of feed line fault initiation and recovery efficiently, considering the various sampling resolutions of feed line fault recording and large amount of fault information. First, the proposed fault detection model is robust even without a detection threshold; it achieves relatively high detection accuracy through adaptive adjustment of Gaussian window width. Second, the preprocessed fault waveforms are converted into time–frequency images. Third, a CNN with a parallel block is proposed as a robust classifier. This method can realize fast convergence by utilizing a new activation function and achieve high accuracy by extracting image features in a wide and short spatial range. Finally, simulation and real measurement data are leveraged in the testing phase to verify the performance of the proposed diagnosis method. The proposed models have great performance on the test database when evaluated using accuracy, recall, precision, and F1-score. Results show that the proposed framework obtains an average accuracy of 99.8 % and 98.3 % for simulation fault cases and real measurement data, respectively. The test results of MCNN are better than 1-D CNN and other well-known classifiers.</div><div>© 2017 Elsevier Inc. All rights reserved.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite time adaptive resilient control method for distributed energy storage systems considering disturbance effects","authors":"","doi":"10.1016/j.ijepes.2024.110315","DOIUrl":"10.1016/j.ijepes.2024.110315","url":null,"abstract":"<div><div>The large-scale application of measurement devices, programmable controllers, and power electronic devices increases the likelihood of distributed energy storage systems suffering from various disturbances, thus affecting the stable operation of the system. In this paper, an adaptive finite time fast resilient control strategy is proposed for the unknown transmission disturbance in the control channel of distributed energy storage system. Firstly, the adverse effects of sensor and actuator disturbances on conventional consensus-based secondary control strategies are quantitatively analyzed. Secondly, an resilient control protocol with adaptive compensation mechanism based on terminal sliding mode is proposed, and the suppression mechanism of the controller to the unknown disturbance of the sensor and the actuator is analyzed, and the finite time convergence of the proposed distributed resilient secondary control strategy is proved theoretically. Experimental results show that the proposed control strategy is correct and effective.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tensor power flow formulations for multidimensional analyses in distribution systems","authors":"","doi":"10.1016/j.ijepes.2024.110275","DOIUrl":"10.1016/j.ijepes.2024.110275","url":null,"abstract":"<div><div>In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of Power flows (PFs) in distribution systems. The presented algorithms are the base for a new TensorPowerFlow (TPF) tool and shine for their simplicity, benefiting from multicore Central processing unit (CPU) and Graphics processing unit (GPU) parallelization. We also focus on the mathematical convergence properties of the algorithm, showing that its unique solution is at the practical operational point. The proof is validated using numerical simulations showing the robustness of the FPI algorithm compared to the classical Newton–Raphson (NR) approach. In the case study, a benchmark with different PF solution methods is performed, showing that for applications requiring a yearly simulation at 1-minute resolution, the computation time is decreased by a factor of 164, compared to the NR in its sparse formulation. Finally, a set of applications is described, highlighting the potential of the proposed formulations over a wide range of analyses in distribution systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced frequency aware microgrid scheduling towards seamless islanding under frequency support of heterogeneous resources: A distributionally robust chance constrained approach","authors":"","doi":"10.1016/j.ijepes.2024.110310","DOIUrl":"10.1016/j.ijepes.2024.110310","url":null,"abstract":"<div><div>This paper introduces an enhanced frequency aware microgrid scheduling (E-FAMS) model designed to achieve seamless islanding (SI) for microgrids after experiencing an unintentional islanding event (UIE). The model addresses uncertainties in load forecasting and demand-side resources’ (DSRs) frequency support, described using a Wasserstein-metric ambiguity set, through the distributionally robust chance constrained (DRCC) approach. It concurrently optimizes unit commitment, generation dispatch, reserve capacity, power exchange, and the frequency response of heterogeneous frequency support resources (HFSRs). A quadratic frequency (QF) approach is proposed to derive sufficient conditions for the maximum frequency deviation (MFD) constraints, which are then convexified using the piecewise linear of multivariable functions (PWL-MFs) technique and integrated into the proposed model. Case study results confirm the effectiveness of the proposed model, providing a novel solution for SI in microgrids.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of coordinated control method based on Graph search method between EV and DG for voltage regulation","authors":"","doi":"10.1016/j.ijepes.2024.110295","DOIUrl":"10.1016/j.ijepes.2024.110295","url":null,"abstract":"<div><div>In this paper, a study is conducted to solve voltage problems that may occur, when large-scale Distributed Generations (DGs) and Electric Vehicles (EVs) are connected to the distribution system, through coordinated control between DGs and EVs. Using the Graph Search Method (GSM), the voltage problem was solved through the reactive power control of EVs and DGs in the near area where the voltage problem occurred. As a result, it was possible to obtain a result with high robustness against the change of the topology and reduction of the total loss of distribution system. In addition, when the voltage problem cannot be solved by only reactive power control, the active power control was performed for EVs and DGs included in a specific divided system of the conventional distribution system using the GSM to maintain the voltage within the normal range. Finally, to verify the performance of the proposed method, the whole algorithm was implemented by linking the Open Source Distribution System Simulator (OpenDSS), and the MATLAB.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating adaptive local search and experience-based perturbed learning into artificial rabbits optimizer for improved DC motor speed regulation","authors":"","doi":"10.1016/j.ijepes.2024.110266","DOIUrl":"10.1016/j.ijepes.2024.110266","url":null,"abstract":"<div><div>The widespread utilization of direct current (DC) motors in real-life engineering applications has led to the need for precise speed control, making controllers a crucial aspect of DC motor systems. Proportional-integral-derivative (PID) controllers have been widely adopted due to their simplicity and effectiveness. However, recent advancements have introduced fractional order PID (FOPID) controllers that offer improved control performance for complex systems with nonlinear dynamics. To fully leverage FOPID controller’s benefits, an efficient tuning method is essential. In this study, we propose artificial rabbits optimization (ARO) algorithm with enhanced strategies, called IARO, to optimize the FOPID controller for DC motor speed regulation. The IARO algorithm incorporates an adaptive local search (ALS) mechanism and an experience-based perturbed learning (EPL) strategy, addressing the shortcomings of ARO and providing better exploration–exploitation balance. We validate the superiority of IARO over competitive algorithms on the CEC2020 benchmark functions, showcasing improved solution stability and consistency. The IARO algorithm is then applied to tune the FOPID controller for DC motor speed regulation. The problem is formulated as a constraint minimization task, optimizing the integral of time-weighted absolute error cost function while adhering to critical design requirements. Comparative simulations demonstrate the IARO algorithm’s ability to achieve superior cost function values and faster convergence compared to other algorithms' based FOPID controllers. The IARO-based FOPID controller exhibits enhanced stability, smoother speed response, larger gain margin, and wider bandwidth compared to other reported algorithms. Additionally, a hardware implementation is also conducted to further validate the practical applicability of IARO based design method. The IARO-based FOPID controller showed remarkable accuracy in tracking multi-step reference inputs and robustly rejected external disturbances, outperforming other recent optimization-based controllers. Additionally, the IARO-based PID controller achieved better performance in key time-domain metrics, including lower overshoot, faster rise time, shorter settling time, and minimized peak time.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards non-virtual inertia control of renewable energy for frequency regulation: Modeling, analysis and new control scheme","authors":"","doi":"10.1016/j.ijepes.2024.110314","DOIUrl":"10.1016/j.ijepes.2024.110314","url":null,"abstract":"<div><div>Currently, when renewable generation participates in frequency regulation, the traditional control method is to emulate synchronous generators through virtual inertia control. However, virtual inertia has a time delay, so essentially, it is a fast power response. Meanwhile, virtual inertia control is likely to be affected by frequency fluctuation since it responds to the derivative of frequency. Hence, it’s worth exploring non-virtual inertia control for renewable energy when participating in frequency regulation. For this reason, a novel two-segment droop control scheme for renewable energy frequency regulation is proposed in this research. Firstly, the extended system frequency regulation (SFR) model, which contains virtual inertia with time delay, is built and analytically solved by order decrement based on the Routh approximation method. Afterwards, according to the analytical solution, time delay affects the frequency response of renewable energy. It can also be analytically proved that the non-virtual inertia control, e.g., sole droop control, could replace virtual inertia under the same frequency deviation. Still, more energy may be needed for frequency regulation. Furthermore, a novel two-segment droop control is presented, and to analytically prove its ability to replace virtual inertia, the impulse function balancing principle and the integration by parts algorithm were adopted to address the initial conditions of the differential equation. Based on the analytical expression, it can be analytically proved that a lower frequency deviation can be obtained under the same frequency regulation energy. Accordingly, a parameter-setting method for two-segment droop control was proposed. Finally, the effectiveness of the proposed method is verified by using a two-area system frequency response model, and the results reveal that it can be used to replace virtual inertia and has better performance.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time detection of insider attacks on substation automation systems using short length orthogonal wavelet filters and OPAL-RT","authors":"","doi":"10.1016/j.ijepes.2024.110311","DOIUrl":"10.1016/j.ijepes.2024.110311","url":null,"abstract":"<div><div>Substation Automation Systems (SASs) integrate communication networks with physical equipment and are vulnerable to cyberattacks. A subset of these attacks, namely Insider attacks, are launched from knowledgeable insiders and therefore they are typically difficult to detect. This paper presents a new method for detecting and classifying Insider cyberattacks as well as power disturbances on SASs using short-length orthogonal wavelet filters in real-time using an OPAL-Real-Time (OPAL-RT) simulator. An Intrusion Detection System (IDS) is proposed in which custom-designed wavelet filters of short length are developed to better extract both the network and physical data of the SASs into time–frequency spectrograms. The advantage of using the short length filters is to provide fast detection of these time-sensitive Insider attacks and disturbances in real-time, which is a key requirement for mitigation to be possible. The generated spectrograms are fed to a Convolutional Neural Network (CNN) that automates the classification process. An experimental dataset is developed from real-time testing using OPAL-RT that implements several types of cyberattacks including Insider attacks and other popular attacks such as Denial-of-Service and False Data Injection as well as challenging attacks such as Replay and Message Suppression attacks. The results of experimentally testing the proposed method in real-time using OPAL-RT demonstrate that the use of the short-length custom-designed orthogonal wavelet filters achieves a detection accuracy of 97.37 % compared to other methods as well as a low runtime of 33.786 ms.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control","authors":"","doi":"10.1016/j.ijepes.2024.110312","DOIUrl":"10.1016/j.ijepes.2024.110312","url":null,"abstract":"<div><div>This paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, torque models of the internal combustion engine (ICE) and electric machine (EM) are developed using a data-driven approach, followed by the construction of fuel consumption and carbon emission models. Second, an event-triggered mechanism, dependent on state prediction error, is introduced and updated at each time step based on the system’s current state. Additionally, a cubature Kalman filter (CKF) is employed to estimate and correct the state prediction error, minimizing inaccuracies. A trade-off coefficient is incorporated to optimize the balance between fuel consumption and carbon emissions. The ET-MPC method results in a 0.68% difference in fuel consumption and 3.43% increase emissions compared to the traditional MPC method. However, ET-MPC significantly reduces computational overhead by 56.66. The ET-MPC method effectively allocates the ship’s energy according to the varying trade-off coefficient, achieving optimal energy management under different constraints.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}