Differential Protection Algorithm Research Articles

Setting-Less Differential Protection of Power Transformers Based on Wavelet Transform

This paper presents a novel algorithm for power transformer differential protection based on wavelet transform (WT) and introduces new indices to distinguish internal faults from normal operating conditions and the occurrence of the inrush current. The proposed setting-less algorithm has no limits on the structure, dimension, capacity, and core type of the transformer. For this purpose, six indices based on fault detection functions extracted from WT transform are presented. Then, weighting factors for the indices by using the least squares method are calculated. In order to validate the proposed method, the approach has been evaluated on four transformers with 2 kVA, 10 kVA, 400 kVA, and 125 MVA. The success rate of fault detection in 10 kVA, and 125 MVA transformers was 100% and in 2 kVA and 400 kVA transformers was 93.33% and 94.44%, respectively. Also, the proposed algorithm has a remarkable capability in fast fault detection to protect the power transformer.

Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks model

The privacy-protected algorithm (PPA) is pivotal in the realm of machine learning, especially for handling sensitive data types, such as medical and financial records. PPA enables two distinct operations: data publishing and data analysis, each capable of functioning independently. However, the field lacks a unified framework or an efficient algorithm to synergize these operations. This deficiency inspires our current research endeavor. In this paper, we introduce a novel dual-mode empirical risk minimization (D-ERM) model, specifically designed for integrated learning tasks. We also develop an alternating minimization differential privacy protection algorithm (AMDPPA) for implementing the D-ERM model. Our theoretical analysis confirms the differential privacy and accuracy of AMDPPA. We validate the algorithm’s efficacy through numerical experiments using real-world datasets, demonstrating its ability to effectively balance privacy with learning efficiency.

AI-driven anonymization: Protecting personal data privacy while leveraging machine learning

AbstractThe development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and reports of criminal attacks and theft. Consequently, the need to achieve intelligent protection of personal information through machine learning algorithms has become a paramount concern. Artificial intelligence leverages advanced algorithms and technologies to effectively encrypt and anonymize personal data, enabling valuable data analysis and utilization while safeguarding privacy. This paper focuses on personal data privacy protection and the promotion of anonymity as its core research objectives. It achieves personal data privacy protection and detection through the use of machine learning's differential privacy protection algorithm. The paper also addresses existing challenges in machine learning related to privacy and personal data protection, offers improvement suggestions, and analyzes factors impacting datasets to enable timely personal data privacy detection and protection.

Design of Laser-Powered Opto-Electrical Current Transformer and Its Application in Electrical Automation of Substations

The novel power equipment represented by the Opto-Electrical Current Transformer (OECT) possesses unparalleled advantages compared to traditional Current Transformers (CTs), rendering it with vast potential in power systems. In the design of OECT, this study employs laser power supply and focuses on the overall perspective, particularly the design of the laser power supply module and the optical fiber digital transmission module. The laser power supply module selects a high-power semiconductor laser to provide power to the circuit, incorporating line filters, thermistor resistors, and a laser overcurrent protection circuit. An automatic temperature control circuit based on PI regulation is utilized to stabilize the working temperature of the laser diode (LD). The photoelectric conversion device PPC-6E achieves a maximum photoelectric conversion efficiency of 40%, and the MAX639 DC–DC conversion chip ensures stable output voltage. The optical fiber digital transmission module adopts a circuit composed of multiple NOR gate logic for electro-optical conversion, a low-power driving circuit based on the 74LVC1G07 chip, and the HFBR-2412 photoelectric converter to simplify the circuit board design. To achieve a higher communication baud rate, a quartz multimode optical fiber with a diameter of 62.5 μm is chosen. In the experimental phase, a simulation of high-power AC power with equal turns is conducted. The linearity/temperature test results demonstrate that the designed OECT in this study meets expectations. In the electrical automation test of substations, a comparison between traditional CTs and the designed OECT is carried out using traditional protection algorithms and optical differential protection algorithms. The results indicate that, compared to traditional protection algorithms, the optical differential algorithm using the designed OECT achieves faster protection times.

Voltage Frequency Differential Protection Algorithm

Advancements in new technologies, a reduction in CO2 emissions, and the rising demand for energy are causing a growth in the share of renewable energy sources. In distribution networks, an increasing number of distributed generators (DGs) makes the utility grid’s protection complex and demanding. Vector surge and rate-of-change-of-frequency are the established anti-islanding protection methods, recognizing that the standard paradigm for protection, involving distributed generation, cannot be set only once but has to be continuously updated following the requirements and changes in the system. One of the requirements is active participation in the preservation of system frequency and voltage, which can be interrupted if the DG trips and disconnects from the utility grid. Anti-islanding protection and spurious tripping can be avoided by implementing new algorithms and techniques. This paper presents a novel protection scheme based on a voltage frequency differential. The proposed algorithm employs remote and local frequency measurements in such a manner that, for the occurrence of a frequency difference, it is assumed that the DG is in an islanding state. In this article, we demonstrate the feasibility of the algorithm through numerical analysis of grid events and laboratory testing emulating real grid-measured values. The test results show that the algorithm is resilient to false tripping for non-islanding events and more reliable than conventional methods in islanding detection. The algorithm can be set to low-frequency differential values, drastically reducing the non-detection zone in any DG type, regardless of its size and voltage level at the point of common coupling. Unlike standard anti-islanding methods, the algorithm supports the ability of the DG to fault-ride through demand.

A watchdog model for physics-based anomaly detection in digital substations

The security of cyber–physical systems (CPS) presents new challenges stemming from computations that work primarily with live physics data. Although there is a body of previous research on detection of malware on CPS, more effective designs are needed to address limitations such mimicry attacks and other forms of evasive techniques. Relay algorithms in particular, such as differential and harmonic protection algorithms, are essential to protecting physical equipment such as power transformers from faults. Relay algorithms, though, are often disabled, altered, or otherwise suppressed by malware.In this paper, we first provide background on the main types of failures that may occur in an electrical power substation after relay algorithms are disabled by malware. We also provide some initial insights into malware methods that involve physics-informed data manipulations, which in turn may lead to power outages and physical damage to power transformers. We then describe the design of a watchdog algorithm that is continuously on the look out for anomalies in the execution time of relay algorithms along with their associated performance counters. We implemented the watchdog approach in Python, and evaluated it empirically on emulations of differential and harmonic protection algorithms on a computing machine.

DP-EPSO: Differential privacy protection algorithm based on differential evolution and particle swarm optimization

In deep learning differential privacy protection, adding noise based on gradient has become a mainstream algorithm, but excessive gradient perturbation noise causes accuracy degradation. To solve this problem, a differential privacy protection algorithm based on differential evolution and particle swarm optimization is proposed to realize hyperparameter optimization in differential privacy, reduce the impact of noise on the model, and effectively improve the accuracy. On the one hand, the differential evolution scheme performs selection, crossover and mutation on learning rate η, make it approach the global optimal solution, and improve the computational efficiency of the algorithm. On the other hand, the particle swarm optimization scheme is adopted. Without changing the parameters and gradient structure, the parameters are optimized by using the network propagation attributes, which reduces the influence of noise on the accuracy. Experiments are performed on three datasets: Cifar10, Mnist and FashionMnist. Compared with the existing differential privacy algorithms, under the same privacy budget, the proposed algorithm has better accuracy and higher efficiency.

Application of Discrete Wavelet Transform and Chebyshev Neural Network for Differential Protection of Indirect Symmetrical Phase Shift Transformer

The Indirect Symmetrical Phase Shift Transformer (ISPST) stands out from a power transformer due to its combination of electrically connected and magnetically coupled circuits. Hence in this work, an intelligent differential protection algorithm, based on Discrete Wavelet Transform (DWT) and Chebyshev Neural Network (ChNN), is proposed as main classifier to discriminate internal fault and inrush. Half cycle thee phase post fault differential current is considered for the proposed algorithm. PSCAD/EMTDC software is utilized to simulate different operating conditions of ISPST, resulting in the simulation of a significant amount of internal faults and inrush cases. The algorithm under consideration has undergone extensive evaluation across numerous cases, resulting in an accuracy rate exceeding 99%. The results indicate that the classifier based on DWT - ChNN yields extremely promising results, even when dealing with a noisy signal, current transformer (CT) saturation, and varying ISPST ratings. Superiority of the proposed algorithm is also compared with Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBFNN) and Probabilistic Neural Network (PNN) based approaches under the same conditions and it is found that proposed classifier is the most efficient and rapid among all alternative classifiers for the differential protection scheme of an ISPST under the considered conditions.

A clustering-based differential privacy protection algorithm for weighted social networks.

Weighted social networks play a crucial role in various fields such as social media analysis, healthcare, and recommendation systems. However, with their widespread application and privacy issues have become increasingly prominent, including concerns related to sensitive information leakage, individual behavior analysis, and privacy attacks. Despite traditional differential privacy protection algorithms being able to protect privacy for edges with sensitive information, directly adding noise to edge weights may result in excessive noise, thereby reducing data utility. To address these challenges, we proposed a privacy protection algorithm for weighted social networks called DCDP. The algorithm combines the density clustering algorithm OPTICS to partition the weighted social network into multiple sub-clusters and adds noise to different sub-clusters at random sampling frequencies. To enhance the balance of privacy protection, we designed a novel privacy parameter calculation method. Through theoretical derivation and experimentation, the DCDP algorithm demonstrated its capability to achieve differential privacy protection for weighted social networks while effectively maintaining data accuracy. Compared to traditional privacy protection algorithms, the DCDP algorithm reduced the average relative error by approximately 20% and increases the proportion of unchanged shortest paths by about 10%. In summary, we aimed to address privacy issues in weighted social networks, providing an effective method to protect user-sensitive information while ensuring the accuracy and utility of data analysis.

A study of user data privacy protection algorithms in the context of metaverse based on emotional AI IoT

Abstract In the context of the metaverse, user data privacy protection has become an important issue. In this paper, firstly, a user data privacy leakage risk assessment scheme is designed by attribute sensitivity calculation, attribute similarity calculation, and attribute association calculation. Then a data privacy protection algorithm based on differential privacy is proposed, and the differential privacy data protection algorithm and implementation mechanism are described. Finally, the performance of the differential privacy protection algorithm is evaluated by analyzing the learning performance and protection performance of the algorithm. The results show that the learning performance of the differential privacy protection model decreases with increasing τ when q = 0 The larger q is, the better the protection performance of the model is, and the optimal τ value also shows a trend to the right. This study provides an effective method for user data privacy protection under the metaverse and offers new ideas for research in related fields.

An enhanced sensitive power differential protection for series compensated transmission lines in renewable generation intensive power systems

Series compensation is widely used to improve the electrical characteristics of long transmission lines and increase the power transfer capability. However, the insertion of a series capacitor introduces various phenomena that affect the performance of traditional distance relays. Moreover, the fault characteristics of renewable generation systems coupled with generation outputs that vary with weather patterns produce additional complications to the protection of transmission lines. This paper proposes the application of an enhanced sensitive power differential protection (SPDP) algorithm to the protection of series compensated transmission lines. The algorithm incorporates a criterion that improves the security of the protection by inhibiting the SPDP algorithm from operating for faults external to the protected line. Performance of the enhanced SPDP algorithm has been evaluated on a test system built in Digsilent Powerfactory software. Various in-zone and out-of-zone fault cases were simulated at different compensation levels and fault resistances. The results showed that the enhanced SPDP protection technique can detect in-zone faults along practically the entire transmission line (99.99 %) for compensation levels up to 90 %. The method operated reliably for faults with resistances up to 330 Ω.

An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services.

Location-based services (LBS) are widely used due to the rapid development of mobile devices and location technology. Users usually provide precise location information to LBS to access the corresponding services. However, this convenience comes with the risk of location privacy disclosure, which can infringe upon personal privacy and security. In this paper, a location privacy protection method based on differential privacy is proposed, which efficiently protects users' locations, without degrading the performance of LBS. First, a location-clustering (L-clustering) algorithm is proposed to divide the continuous locations into different clusters based on the distance and density relationships among multiple groups. Then, a differential privacy-based location privacy protection algorithm (DPLPA) is proposed to protect users' location privacy, where Laplace noise is added to the resident points and centroids within the cluster. The experimental results show that the DPLPA achieves a high level of data utility, with minimal time consumption, while effectively protecting the privacy of location information.

Differential privacy protection algorithm for network sensitive information based on singular value decomposition

In order to reduce the risk of data privacy disclosure and improve the effect of information privacy protection, a differential privacy protection algorithm for network sensitive information based on singular value decomposition is proposed. TF-IDF method is used to extract network sensitive information text. By comparing the word frequency of network sensitive information, high word frequency word elements in network information content are collected to obtain the mining results of network sensitive information text. According to the decision tree theory, the equal difference privacy budget allocation mechanism is improved to achieve equal difference privacy budget allocation. By discarding some small singular values and corresponding spectral vectors, the data can be disturbed, and the availability of the original data can be retained, so that it can truly represent the original data set structure. According to the results of equal difference privacy budget allocation and singular value decomposition disturbance, the data of high-dimensional network graph is reduced by random projection, singular value decomposition is performed on the reduced data, and Gaussian noise is added to the singular value. Finally, the matrix to be published is generated through the inverse operation of singular value decomposition to achieve differential privacy protection of network sensitive information. The experimental results show that the privacy protection quality of this algorithm is high and the data availability is effectively improved.

Differential Privacy-based Personalized Recommendation Service for "Helping Farmers" Tourism in Guizhou Province

The revitalization of the countryside is an important strategy proposed in the report of the 19th Party Congress. Rural tourism in Guizhou Province is the key to the development of "big tourism" in the 14th Five-Year Plan of the province, and also the driving force of rural revitalization. Most of the current rural tourism personalized recommendation systems rely on user information, and the exposure of sensitive user information has become an obstacle to the development of smart tourism. Through the genetic matrix decomposition recommendation algorithm based on satisfying differential privacy protection and differential privacy recommendation algorithm for sparse data, privacy protection of similarity calculation, accuracy of recommendation and data security are realized. Actively develop secure and personalized rural tourism recommendations so that the majority of farmers can benefit from them.

Deep learning-based data privacy protection in software-defined industrial networking

The industrial Internet connects equipment to the network and utilizes the data generated to assist businesses. Industrial big data is the result of data accumulation; thus, the industrial Internet has to adopt new technologies—namely, software-defined industrial networks (SDIN) —to keep up with these developments. This study suggests a deep differential privacy data protection algorithm based on SDIN. The deep learning model is analyzed and integrated with differential privacy to provide the process framework for the deep differential privacy data protection algorithm. The equivalent model of the generative adversarial network is used to allow the attacker to obtain the optimal fake samples. The balance between dataset availability and privacy protection is achieved by implementing parameter tuning on the deep differential privacy model. The experimental results show that the proposed algorithm has strong industrial data privacy protection and high data availability and can effectively guarantee the privacy security of industrial data.

Research on Information Leakage Tracking Algorithms in Online Social Networks

An online social network is a platform where people can communicate with friends, share information, speed up business development, and improve teamwork. A large amount of user privacy information existing in real social networks is leaked from person to person, and this issue has hardly been studied. With the rapid expansion of the network, the issue of privacy protection has received increasing attention. So far, many privacy protection methods including differential protection algorithms, encryption algorithms, access control strategies, and anonymization have been researched and applied. Information leakage means that the information shared by the user is disseminated or downloaded by his friends without the user's consent, and the transmission of private information will not be recorded. In order to track and find out the ways and methods of information leakage, this article adopts an unusual method, namely, the probability judgment based on trust. By screening the similarities between users, past information exchanges, and the topology of social networks, a trust model is established to evaluate and estimate the degree of trust between users. According to the rating information privacy of friends' trust, an information dissemination system is established, which can be applied to online social networking platforms to reduce the risk of information leakage, thereby ensuring the security of users' private information. At the same time, this paper expands the transmission system model without user authorization and proposes a fingerprint-based deterministic leak tracking algorithm.

Internal fault probability-based time domain differential protection applied to transmission lines connecting battery energy storage stations

Battery energy storage station (BESS) presents disparate fault current characteristics in charge and discharge states. Classic and recently proposed differential protection algorithms, including phasor-based and time-domain algorithms, are heavily influenced by operating state of BESS. Problems such as low sensitivity and ineffectiveness may arise, especially in charge state. In response to this issue, this paper applies Bayes' Theorem to calculate the probability of internal fault occurrence, named as internal fault probability. In this article, fault current characteristics of BESS under different operating states are described, and then the causes of performance problems of classic and recently proposed differential protection algorithms are analyzed. Subsequently, virtual operating current vector is developed, and the difference of virtual operating current vector in time-domain waveform under various conditions is described. On this basis, internal fault probability-based differential protection (IFP-DP) is presented. Besides, its operating performances under internal and external faults are analyzed. The advantageous performance of IFP-DP is fully validated by changing fault location, type, resistance, operating state and considering outliers, current transformer (CT) saturation, and loss of data. Proposed IFP-DP exhibits gratifying performance in operating time, sensitivity and security in the presence of BESS.

Research on Online Social Network Information Leakage-Tracking Algorithm Based on Deep Learning.

The rapid iteration of information technology makes the development of online social networks increasingly rapid, and its corresponding network scale is also increasingly large and complex. The corresponding algorithms to deal with social networks and their corresponding related problems are also increasing. The corresponding privacy protection algorithms such as encryption algorithm, access control strategy algorithm, and differential privacy protection algorithm have been studied and analyzed, but these algorithms do not completely solve the problem of privacy disclosure. Based on this, this article first searches and accurately filters the relevant information and content of online social networks based on the deep convolution neural network algorithm, so as to realize the perception and protection of users' safe content. For the corresponding graphics and data, this article introduces the compressed sensing technology to randomly disturb the corresponding graphics and data. At the level of tracking network information leakage algorithm, this article proposes a network information leakage-tracking algorithm based on digital fingerprint, which mainly uses relevant plug-ins to realize the unique identification processing of users, uses the uniqueness of digital fingerprint to realize the tracking processing of leakers, and formulates the corresponding coding scheme based on the social network topology, and at the same time, the network information leakage-tracking algorithm proposed in this article also has high efficiency in the corresponding digital coding efficiency and scalability. In order to verify the advantages of the online social network information leakage-tracking algorithm based on deep learning, this article compares it with the traditional algorithm. In the experimental part, this article mainly compares the accuracy index, recall index, and performance index. At the corresponding accuracy index level, it can be seen that the maximum improvement of the algorithm proposed in this article is about 10% compared with the traditional algorithm. At the corresponding recall index level, the proposed algorithm is about 5–8% higher than the traditional algorithm. Corresponding to the overall performance index, it improves the performance by about 50% compared with the traditional algorithm. The comparison results show that the proposed algorithm has higher accuracy and the corresponding source tracking is more accurate.

A new differential protection algorithm for transmission lines connected to large-scale wind farms

The fluctuated output power of the large-scale wind farms (WF) and their fault transient characteristics have an adverse effect on the current differential protection systems of the transmission lines. With increased transmission line length and in the case of weak output power from the WF, the differential current between both line ends increases in normal conditions since the capacitive current is comparable to the load current. This paper proposes a new differential protection algorithm for transmission lines connected to large-scale wind farms. The proposed current differential protection algorithm is developed based on the signs of the phase current samples at both line ends, instead of utilizing directly the current magnitudes. The similarity between the signs of the phase current samples at both line ends is evaluated utilizing the signed correlation criterion, and the fault detection index is calculated to discriminate the internal faults from other events. In addition, a new online technique is introduced to adjust the protection settings considering the different output power levels of the WF. The conducted PSCAD/EMTDC simulation studies confirm the acceptable performance of the proposed protection algorithm for numerous normal and fault scenarios, including different fault resistances and inception angles as well as all fault types. The effect of line length, type of wind turbine generator, and different values of WF output power are also considered.

A New Differential Current Protection Algorithm for Three-Terminal Line

A New Differential Current Protection Algorithm for Three-Terminal Line