Fault Lines Research Articles

Correction: Integrated dynamic spiking neural P systems for fault line selection in distribution network

Correction: Integrated dynamic spiking neural P systems for fault line selection in distribution network

Security constrained optimal power shutoff for wildfire risk mitigation

AbstractElectric grid faults are increasingly the source of ignition for major wildfires. To reduce the likelihood of such ignitions in high risk situations, utilities use preemptive de‐energization of power lines, commonly referred to as Public Safety Power Shutoffs (PSPS). Besides raising challenging trade‐offs between power outages and wildfire safety, PSPS removes redundancy from the network at a time when component faults are likely to happen. This may leave the network particularly vulnerable to unexpected line faults that may occur while the PSPS is in place. Previous works have not explicitly considered the impacts of these outages. To address this gap, the Security Constrained Optimal Power Shutoff problem is proposed which uses post‐contingency security constraints to model the impact of unexpected line faults when planning a PSPS. This model enables, for the first time, the exploration of a wide range of trade‐offs between both wildfire risk and pre‐ and post‐contingency load shedding when designing PSPS plans, providing useful insights for utilities and policy makers considering different approaches to PSPS. The efficacy of the model is demonstrated using the EPRI 39‐bus system as a case study. The results highlight the potential risks of not considering security constraints when planning PSPS and show that incorporating security constraints into the PSPS design process improves the resilience of current PSPS plans.

Seismic Performance of Bridge Expansion Joints with and without Viscous Dampers during the 6 February 2023 Kahramanmaraş Earthquakes

Expansion joints render bridge structures highly vulnerable to damage during strong ground motions. Failures of expansion joints triggered by earthquakes not only jeopardize the post-earthquake serviceability of the bridge but also have a significant impact on the bridgeâs overall seismic performance. Despite extensive investigations and efforts to integrate these measures into design specifications aimed at mitigating the consequences of relative movements between adjacent bridge spans, major earthquakes have still revealed instances of damage related to expansion joints. On 6 February 2023, strong earthquake sequences occurred in KahramanmaraÅ, Turkey, with magnitudes of M7.7 and M7.6. The fault lines and epicenters of these shallow earthquakes were near the city and town centers and caused severe structural damage to buildings and infrastructures. There are approximately 1000 railway and highway bridges in the earthquake-affected region. Although both highway and railway bridges have generally performed well, some bridges experienced structural damage during the KahramanmaraÅ earthquakes. A large number of damage on the bridges is due to pounding and opening relative movements in expansion joints. This paper presents a comprehensive seismic evaluation of expansion joint failure mechanisms on bridges without viscous dampers during the 2023 KahramanmaraÅ earthquake sequences and an in-depth investigation into the seismic performance of bridge expansion joints equipped with viscous dampers and shock transmission unit devices are implemented utilizing the strong ground motion data collected throughout the earthquake sequences. It can be stated that the near-fault induced significant directivity and fling effects, resulting in notable velocity pulses and permanent tectonic deformations, and that these effects contributed to the failures of expansion joints, viscous damper devices, pot bearings, and shear keys.

Design of Power Outage Monitoring and Warning System Based on Internet of Things

With the rapid development of distribution network and the high popularity of distributed generation, the research on risk assessment of distribution network is accelerated. When quantifying the degree of harm caused by abnormal situations, it is necessary to consider the severity of the harm and the impact on society to measure the severity of power failure and reduce the loss caused by power risk. To provide data support for the fine perception of the situation and its development trend, the monitoring system constructed by the power microservice further improves the ability and accuracy of sudden fault prediction. In this paper, through the monitoring system information to explore the transmission line fault early warning, the data analysis model is established. The particle swarm optimization algorithm and BP neural network are used to build the early warning model to predict the potential fault situation and cause analysis of the power grid under various indicators in a certain period in the future, which is to provide more scientific and reasonable risk management and control for the power grid. Through the statistical analysis of all kinds of information data collected by the power microservice, a power monitoring system based on early warning of power outage risk is established to enhance the operation adaptability of the power grid system under uncertain conditions and prevent the occurrence of tripping blackouts so that this study has an important function of intelligent decision-making. The experimental results show the feasibility and high efficiency of this study. The comparison of algorithm results shows that the performance of this method is improved by more than 10%. The stability is improved by 17%. The response time to various situations is shortened by 32% to judge the abnormal operation state of the power grid more efficiently. It can make emergency preparations in time, reduce the damage caused by accidents to the power grid and provide practical tools for the power grid to further enhance the disaster prevention ability of transmission equipment and effectively resist risk events.

A single-ended high-impedance fault protection scheme for hybrid HVDC transmission lines based on coordination of control objective

Hybrid HVDC transmission technology advancement has led to significant progress. A reliable and effective DC line protection scheme is crucial for DC transmission systems. The traditional protection schemes suffer from insufficient sensitivity and reliability during high-impedance faults on DC lines. To address the issue, this study introduces a hybrid HVDC line single-ended protection scheme based on the coordination of converter control objectives. In the fault-ride-through(FRT) stage, different control strategies and objectives are applied to the converter based on the fault pole and fault direction identification results. Internal and external faults are discerned by calculating measured voltage with the coordination of the converter control objective. This approach maintains heightened sensitivity to high-impedance DC line faults. Moreover, it operates independently of double-ended communication and demands a low sampling rate. Extensive simulation results robustly substantiate the efficacy of the proposed protection scheme.

A physics-informed data-driven fault location method for transmission lines using single-ended measurements with field data validation

Data driven transmission line fault location methods have the potential to more accurately locate faults by extracting fault information from available data. However, most of the data driven fault location methods in the literature are not validated by field data for the following reasons. On one hand, the available field data during faults are very limited for one specific transmission line, and using field data for training is close to impossible. On the other hand, if simulation data are utilized for training, the mismatch between the simulation system and the practical system will cause fault location errors. To this end, this paper proposes a physics-informed data-driven fault location method. The data from a practical fault event are first analyzed to extract the ranges of system parameters such as equivalent source impedances, loading conditions, fault inception angles (FIA) and fault resistances. Afterwards, the simulation system is constructed with the ranges of parameters, to generate data for training. This procedure merges the gap between simulation and practical power systems, and at the same time considers the uncertainty of system parameters in practice. The proposed data-driven method does not require system parameters, only requires instantaneous voltage and current measurements at the local terminal, with a low sampling rate of several kHz and a short fault time window of half a cycle before and after the fault occurs. Numerical experiments and field data experiments clearly validate the advantages of the proposed method over existing data driven methods.

Digital battleground: An examination of anti-refugee discourse on Twitter against Ukrainians displaced by Russia’s invasion of Ukraine

Russia’s war of aggression in Ukraine has triggered Europe’s largest refugee crisis since World War II. In this case study, we investigate the prevalence and types of anti-refugee discourse about Ukrainian refugees on Twitter. Previous studies primarily focused on public discourse and attitudes toward racialized refugees and immigrants; the Ukrainian refugee crisis is unique in that it is one of the few instances of a recent refugee crisis involving people who do not come from mostly racialized communities. Using Communalytic, a computational social science tool for studying public discourse on social media, we automatically collected and identified toxic posts mentioning Ukrainian refugees during the first year of Russia’s full-scale invasion of Ukraine. We focused on posts containing toxic language, as this is where we are most likely to find examples of anti-refugee sentiments. Based on a manual analysis of 2,045 toxic posts referencing Ukrainian refugees, the most prevalent ones were politically motivated and included partisan content (33 percent), followed by posts containing expressions countering anti-refugee narratives (20 percent). These findings highlight the escalating politicization and polarization of discussions about Ukrainian refugees both online and offline. Furthermore, 53 percent of the sample aligned with pro-Kremlin narratives against Ukraine. By exploiting anti-refugee sentiments and leveraging existing political and cultural fault lines in the West, pro-Kremlin messages on Twitter contribute to diminishing support for Ukrainian refugees, minimizing the severity of the war, and undermining international support for Ukraine.

Insights into Afikpo Synclinorium structures: Subsurface analysis and intrusion outlining from airborne magnetic data

The airborne magnetic data over Afikpo Synclinorium were used to highlight subsurface structures and establish the orientations of tectonic features and their influence on the hydrocarbon play of the area. The characteristic trends of the lineaments were achieved through the center for exploration targeting of grid data analysis. The results of the TMI, residual magnetic field, first vertical, horizontal derivative, Analytic signal, and Tilt angle derivative classified the area into regions of high magnetic intensity observed around the northeastern, southwestern, and central portions of the study area. These areas characterized by anomalous signatures of short magnetic wavelengths delineate shallow magnetic sources. Conversely, regions with medium to low magnetic intensities are characterized by long magnetic wavelengths which indicates deep-seated magnetic source(s) and prospect for hydrocarbon (if other play concepts are emplace); due the thick sediment cover at the southern portion of the area (around, southern part of Nguzu, Ohafia, Biakpan, Abriba and Bende areas). The sediment thickness decreases towards north, west, North-east and north-west (dominated by Benue Trough deposit). Depth to the anomaly sources is of two categories, namely, shallow <1.0 km (dominated by NE trending structures) and deep ≥2.5 km. Ground-truthing confirmed the anomalous zone of high magnetic sources in the northeastern and central regions as Tertiary related tectonic-magmatic intrusion. The most prominent intrusion cuts across Amangwu Edda in Afikpo South through Amaeta-Oziza to Cross River State in a fissure form. This intrusion occurrence conformed to the high magnetic signature identified as a dolerite sill within the Afikpo Sub-basin. It stretches over 16 km in length with an average width of about 1.2 km, having its widest part in the northern parts of Mata Hospital Afikpo. The subsurface topographic model indicated that no fewer than 50% of the study area is characterized by sporadically distributed intrusive rocks. The result revealed several structural lineaments with high lineament density at the northcentral corner of the area trending NE-SW with fault lines (slip fault) perpendicular to it, offsetting the Akpoha, Ibii, Amasiri. and Ozara-ukwu ridges. The major drainage systems across the area followed major structural trends perpendicular to the identified major lineaments, passing through the major regional structural displacement pattern of the Basin. The emplacement of these intrusions might have impacted the hydrocarbon potential of the basin.

Improvement of the Evaluation of Seismic Risk in Fault Areas by Lidar-Derived Geophysical Data

This study aimed to improve the methods of assessing seismic risk in fault zones based on lidar data in geophysics. The research highlighted this by comparing the newly developed fault maps with the usual methods of fault mapping and how lidar technology developed high-resolution 3D mapping. We conducted mobile and terrestrial LIDAR surveys to produce DEMs and study the attributes of the fault zones. The technique involved mobile lidar systems with different specifications of emitted transmission rate: 45 000 m/s to 52, 100m/s pulse repetition: 190, 000 Hz–220, 000 Hz; and point density: 10223 points/m2 to 14567 points/m2. Terrestrial lidar surveys used scanner heights of 1. 500-1. 700m and obtained the horizontal and vertical sampling density, ranging from 240,456 to 315,678 points per square meter. We used LAStools, Arc GIS, and QISIS software to filter, classify, and visualize the data processing. e applied interpolation techniques such as IDW, Kriging, Spline, and Natural Neighbors to generate DEMs. Research outcomes identified 15 different fault segments with lengths varying from 10. 000-20. 000 km, along with maximum displacements of 0. 987-4. 567 m, and average slip rates of 3. 456-7. 890 mm/year. The most extended fault segment altogether was FS05, which was 20. 000 km with a maximum bidding distance of 4. 567 m and a 7. 890 mm/year slip rate. We discovered that the proposed method successfully filtered out noise points from lidar data, with the noise points varying between 0.111-0.266 million. We created DEMs with vertical rms errors ranging from 0.045-0.050 m. The study revealed that lidar technology offers accurate and dense geospatial data, essential for discriminating between fault zones. This approach dramatically improves seismic hazard analysis and the identification of the best ways to minimize risks. These are increasing lidar surveys in other seismically active regions, using multiple data sources for analysis, and deploying constant surveys in high-risk fault line regions to increase consistency in detecting surface changes and tectonic activity.

Non-linear ground deformation detection and monitoring using time series InSAR along the coastal urban areas of Pakistan.

Conventional geodetic methods rely on point measurements, which have drawbacks for detecting and tracking geologic disasters at specific locations. In this study, the time series Interferometric Synthetic Aperture Radar (InSAR) approach was incorporated to estimate non-linear surface deformation caused by tectonic, shoreline reclamation, and other anthropogenic activities in economically important urban regions of Pakistan's southern coast, which possesses around 270km. The shoreline is extended from the low-populated area on the premises of the Hub River in the west to the highly populated Karachi City and Eastern Industrial Zone, where we collected the Sentinel-1A C-band data from 2017 to 2023 to address urban security and threats to human life and property. The main advantage of opting for the non-linear persistent scatterer interferometric SAR (PSInSAR) approach for this study is that it exposes minute movements without any prior consideration of conventional monitoring techniques, making it valid in continuously varying regions. An average vertical displacement range of - 170 to + 82mm per year was found, which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience an annual subsidence of 170mm, and the less populated western region experiences an uplift of 82mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. Groundwater table-depleting regions experienced high levels of land subsidence, and tectonic activities controlled vertical displacement in the region. Major variation was detected after an earthquake occurred along fault lines. This study was designed because a non-linear approach is required to address ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively.

PMSG-based wind farm high-voltage ride-through improvement for single-pole tripping of circuit breakers

For transient single phase-to-ground (SPG) faults in transmission lines, single-pole circuit breaker (SPCB) operation can improve system reliability. This issue is more critical in transmission lines emanating from wind farms (WFs). In the large-scale WFs, the grid codes necessitate that wind generation remains in operation during the transmission line faults. For this purpose, the grid codes focus on the development of fault-ride-through (FRT) requirements. In this paper, it is shown that after the SPCB operation, the terminal voltage of the permanent magnet synchronous generator (PMSG)-based wind turbine (WT) rapidly increases and may cross the high-voltage ride-through (HVRT) boundary. Therefore, the WT terminal overvoltage after the SPCB operation may result in undesirable tripping of the WT. The obtained results in this paper show that in most cases both the coupled and decoupled sequence controllers of the WT converter equipped with the FRT requirements are not able to improve the HVRT performance after the SPCB operation. To enhance the HVRT performance of PMSG-based wind turbines after the SPCB operation, a dc-link chopper controller is proposed. EMTP-RV simulation results on the IEEE 118-bus and 5-bus systems show the success and effectiveness of the proposed method.

Resonance modeling of the tsunami caused by the Aegean Sea Earthquake (Mw7.0) of October 30, 2020

The resonance of tsunami waves in semi-enclosed bays is paramount in understanding and mitigating the impact of seismic events on coastal communities. Semi-enclosed bays, characterized by their partial enclosure, can amplify the effects of incoming tsunami waves due to resonance behavior, where the natural frequencies of the bay correspond to those of the incoming waves. This resonance phenomenon can significantly increase wave height and inundation levels, posing an increased risk to nearby settlements and infrastructure. Understanding the resonance patterns in these bays is crucial for accurate hazard assessment, early warning systems, and effective disaster preparedness and response strategies. On October 30, 2020, an earthquake occurred between the Turkish Bay of Seferihisar Bay and the Greek island of Samos in the Aegean Sea. Long waves generated by the normal-faulting earthquake caused notable damage to settlements within Seferihisar Bay and the north coast of Samos Island. According to the measurements of the Syros mareograph stations, the wave heights were between 2 and 20 cm and wave periods between 9 and 20 seconds. Based on on-site survey reports conducted after the earthquake, inundation was reported in six settlements within Seferihisar Bay. However, inundation was notably higher in Sığacık and Akarca, reaching 2–3 times higher than in other locations, and the water level reached 2 m high. Given that the variance in inundation levels is attributed to resonance phenomena in Sığacık and Akarca rather than the propagation of tsunami waves, this study focused on conducting wave resonance modeling in Seferihisar Bay. The resonance modeling was performed using the RIDE wave model. Furthermore, the research has been expanded to assess the resonance patterns that might emerge in the event of an alternative earthquake or underwater landslide along the fault line responsible for the seismic event, encompassing wave periods ranging from T = 1–9 minutes and T = 20–30 minutes. Modeling results revealed that on the day of the earthquake, wave heights in Sığacık Marina and Akarca surged by 8.5 times in comparison to the wave height at the epicenter. This increase is notably higher, ranging from 2 to 2.5 times, compared to calculations made for other locations (Demircili, Altınköy, and Tepecik). Consequently, it was concluded that one of the reasons for the heightened effectiveness of inundation in Sığacık and Akarca was attributable to resonance. Moreover, supplementary investigations have indicated that waves with a period of T<9 minutes will pose higher risks for Demircili, Altınköy, Sığacık Marina, and Tepecik compared to the day of the earthquake. By comprehensively studying wave resonance in semi-enclosed bays, researchers and policymakers can better anticipate the potential impact of tsunami events and take measures to protect coastal communities, ultimately increasing resilience and reducing the loss of life and property in vulnerable regions.

Advancements In Machine Learning Algorithms for Enhanced Fault Analysis and Categorization in Power Systems

With the growing number of sources, complexity, and dynamic elements of power systems that are prone to disturbance and electrical faults, conventional methods of fault detection are lagging in power system protection. Major faults are exposed to transmission lines due to several factors, such as climate change, heat, lightning strokes, sudden spikes of current and voltage etc. Protecting these lines from faults is cumbersome using traditional methods. However, with the increasing complexity of transmission lines in power systems, advanced technologies such as machine learning come into play for prior fault detection and identification. ML is becoming a popular and intelligent technique for monitoring and predicting health and diagnosing faults in transmission lines of power systems. Since ML is evolving at a faster pace and given the need for and growth of advanced technologies like ML and artificial intelligence, this paper emphasizes comparative analysis of various ML algorithms for transmission line fault analysis. Comparisons for various line faults (LL, LLG, LG, LLL, LLLG, no fault) in power systems using machine learning techniques have been done in terms of accuracy, precision, and other performance measures. The results of the accuracy of various algorithms are represented as tabulated facts in this paper. The Python programming language has been utilized over a dataset to calculate results. For each algorithm, the confusion matrix shows the accuracy of predicting faults over the testing data after training the model or algorithm on the training data. Upon completion of analysis and comparison of MLT's like LR, SVM, DT, RF, K-NN, and gradient descent for analysis of transmission line fault detection, RF and DT machine learning algorithms provided the best accuracy and results. Finally, this paper gives a brief overview of various line faults, ML algorithms accuracy for each transmission fault in a confusion matrix and tabular format and concluded the best machine learning algorithms for power system transmission line fault identification.

Uncovering the uncoverers: identity, performativity and representation in counter-disinformation discourse

ABSTRACT Treating counter-disinformation activities as a discursive practice producing knowledge in specific cultural and historical contexts, this article brings a vital cultural studies perspective to an incipient Critical Disinformation Studies agenda. At the same time, it cautions against inadvertently aligning that agenda with the ‘bad faith’, right-wing populist critique of counter-disinformation practices, acknowledging their genuine potential to enhance democratic culture. It proceeds by interrogating the communicative strategies informing the proliferating Western counter-disinformation initiatives which have mushroomed in response to what is widely perceived to be one of democracy’s most potent threats, and which border on a ‘moral panic’ serving to conceal key ideological tensions. Grounded in Critical Discourse Analysis methods, a close reading of mission statements and related materials from the websites of four leading counter-disinformation units demonstrates how these texts interweave three functions – identity construction, performativity, and representation – to generate the overarching ‘discursive formation’ or ‘articulation’ constituting counter-disinformation as modern liberal democracy’s core, but also exposing its fault lines. Specifically, it shows that a failure to reconcile competing accounts of truth (as scientific rationality and normative value) indexes deeper contradictions pitting the liberal emphasis on individual freedom and capitalist efficiency against the democratic prioritizing of popular power, dispassionate observation against civic participation, and the promotion of transparency against the creeping power of surveillance. It concludes that the absolutist epistemology – a vestige of disinformation’s Cold War conceptual origins – designed to cover over these paradoxes masks the complex roles played by truth and deception in politics, thereby hindering the emergence of alternative, oppositional forms of knowledge capable of embracing truth in its radical contingency and plurality and facilitating democratic renewal.

Integrated dynamic spiking neural P systems for fault line selection in distribution network

Integrated dynamic spiking neural P systems for fault line selection in distribution network

The politics of carbon management in Austria: Emerging fault lines on carbon capture, storage, utilization and removal

With the proliferation of net zero targets in climate policy and corporate climate governance, the question of how to deal with greenhouse gas emissions considered hard-to-abate is attracting growing attention. In this context, the notion of carbon management has emerged on the political agenda – an umbrella term typically encompassing carbon capture and storage (CCS), carbon capture and utilization (CCU) and carbon dioxide removal (CDR). Here, we investigate the emerging politics around the design of a national carbon management strategy in Austria, a country which can be considered a yet understudied latecomer regarding CCS and novel CDR methods including BECCS and DACCS. Based on expert interviews with policy actors as well as qualitative document analysis, we identify four actor coalitions as well as key fault lines. These fault lines specifically relate to a) whether the CCS ban in Austria should be lifted, and b) which economic sectors get access to carbon storage and transport infrastructure as well as subsidy schemes (selective vs. unrestricted integration). We also find that framing CCS, CCU and CDR under the umbrella term carbon management produces specific coalitional effects while simultaneously concealing contested climate policy choices – a finding with wider implications beyond the Austrian case.

A wide area backup protection algorithm based on fault current comparison and evidence theory fusion

Abstract Wide-area backup protection using information in the area to determine the fault location comprehensively requires a high fault tolerance ability. However, the traditional wide area backup protection algorithm based on evidence theory assigns a separate value to each adjacent line for its fault probability, and the performance of the algorithm degrades in the condition of more information loss or errors. To solve these problems, this paper proposed a wide area backup protection algorithm based on fault current comparison and improved evidence theory fusion. The proposed method first selects the suspected fault lines by sequence voltage sorting. Then, the information on the fault current sequence component and traditional protection action is collected and preprocessed to form the evidence set, and its basic probability assignment is calculated. Finally, the improved evidence theory is used for fusion to identify fault lines. Simulation results show that the algorithm can accurately identify fault lines under complex operating conditions, multiple information loss and errors, and has high fault tolerance.

Transmission line fault diagnosis based on SENet-ResNext-LSTM

Transmission line fault diagnosis based on SENet-ResNext-LSTM

Prediction of Oil Source Fault-Associated Traps Favorable for Hydrocarbon Migration and Accumulation: A Case Study of the Dazhangtuo Fault in the Northern Qikou Sag of the Bohai Bay Basin

In order to study the distribution pattern of oil and gas near the lower-source, upper-storage type of oil source faults in the hydrocarbon-bearing basins, a set of prediction methods favourable to oil and gas migration and accumulation were established by superimposing the parts of the oil source fault-associated traps, the contiguously distributed sand bodies and the lateral sealing position of faults. The trap associated with a fault can be determined by the fault’s convex part on the fault plane’s morphology map, the fault throw displacement curve and the intersection of faults on the structure map. The set of sand bodies can be determined by the sand-to-shale ration of the formation. The lateral sealing position of faults can be investigated by the shale content of the fault. This study is based on our case study of the Dazhangtuo Fault in the lower sub-member of the 1st member (Es1L) of the Shahejie formation in the northern Qikou Sag of Bohai Bay Basin. The results illustrate 4 fault nose traps formed by fault line deflection in the Es1L formation of the Dazhangtuo Fault, 2 each in the middle and eastern end. The Dazhangtuo Fault is favorable for oil and gas migration except at the eastern and western ends and the middle part of the fault. The fault-associated traps in the Es1L formation that are highly favorable for hydrocarbon migration and accumulation (overlapping site of associated traps and favorable location for oil and gas migration) are distributed in the eastern and central parts of the Dazhangtuo Fault. In contrast, those moderately favorable for hydrocarbon migration and accumulation (associated trap at a certain distance from the favorable location for oil and gas migration in the Dazhangtuo Fracture) are locally distributed in the east. Both traps are conducive to accumulating hydrocarbons from the underlying source rock in the Es3 formation. Such observations are consistent with the current confirmed hydrocarbon distribution, thus validating the feasibility and accuracy of predicting the distribution of traps related to oil source faults favorable for hydrocarbon migration and accumulation, it can be used to guide the exploration of the lower-source, upper-storage type of hydrocarbon accumulations in the hydrocarbon-bearing basins.

Monitoring UAV status and detecting insulator faults in transmission lines with a new classifier based on aggregation votes between neural networks by interval type-2 TSK fuzzy system

Monitoring UAV status and detecting insulator faults in transmission lines with a new classifier based on aggregation votes between neural networks by interval type-2 TSK fuzzy system