System Bus Research Articles

Determination of optimal size and location of static synchronous compensator for power system bus voltage improvement and loss reduction using whale optimization algorithm

Power systems are usually expected to become heavily loaded as the demand for electrical energy grows and economic consideration limits the installation of additional transmission and generating capacity. Keeping the bus voltage in the power system within the standard permissible limits is an important concern to improve the voltage stability and avoid voltage collapse of the whole power system. The common and effective way to achieve this purpose is by adding flexible AC transmission line devices to the power system. One of these devices is static synchronous compensator. In this paper an approach is proposed to find optimal location and size of static synchronous compensator for improving bus voltage in the power system. A load flow is conducted to identify the low voltage buses which are the weak buses in the system and they are considered as suitable buses for static synchronous compensator connection. An objective function is formulated for optimization process which contains four parts, the voltage deviation, static synchronous compensator size, active and reactive power losses of the whole power system. Whale optimization algorithm is used for the optimization process. The proposed approach is applied on the real power system of Kurdistan Region using power system simulator for engineering software for simulating the power system and finding the optimal size and location of static synchronous compensator for bus voltage improvement. The results are encouraging for applying the approach to any power system. What distinguishes this method is that it accomplishes two things, namely reducing the bus voltage deviation to zero which means that all bus voltages are within the permissible limits and minimizing losses as well.

Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework

This study proposes a probabilistic framework to infer passengers’ responses to unplanned urban rail service disruptions using smart card data in tap-in-only public transit systems. We first identify 19 possible response behaviors that passengers may have based on their decision-making times and locations (i.e, the stage of their trips when an incident happened), including transferring to a bus line, canceling trips, waiting, delaying departure time, etc. A probabilistic model is proposed to estimate the mean and variance of the number of passengers in each of the 19 behavior groups using passengers’ smart card transactions. The 19 behavioral responses can be categorized from two aspects. From the behavioral aspect, they can be grouped into 5 aggregated response behaviors including using bus, using rail (changing or not changing route), not using public transit, and not being affected. The inference of the 19 behaviors can be classified into four cases based on the information used (historical trips vs. subsequent trips) and the context of the observed transactions (direct incident-related vs. indirect incident-related). The public transit system (bus and urban rail) of the Chicago Transit Authority (CTA) is used as a case study based on a real-world rail disruption incident. The model is applied with both synthetic data and real-world data. Results with synthetic data show that the proposed approach can estimate passengers’ behavior well. The mean absolute percentage error (MAPE) for the estimated expected number of passengers in each behavior group is 20.5%, which outperforms the rule-based benchmark method (60.3%). The estimation results with real-world data are consistent with the incident’s context. An indirect model validation method using demand change information and incident log data demonstrates the reasonableness of the results.

Modeling Transport of SARS-CoV-2 Inside a Charlotte Area Transit System (CATS) Bus

We present in this paper a model of the transport of human respiratory particles on a Charlotte Area Transit System (CATS) bus to examine the efficacy of interventions to limit exposure to SARS-CoV-2, the virus that causes COVID-19. The methods discussed here utilize a commercial Navier–Stokes flow solver, RavenCFD, using a massively parallel supercomputer to model the flow of air through the bus under varying conditions, such as windows being open or the HVAC flow settings. Lagrangian particles are injected into the RavenCFD predicted flow fields to simulate the respiratory droplets from speaking, coughing, or sneezing. These particles are then traced over time and space until they interact with a surface or are removed via the HVAC system. Finally, a volumetric Viral Mean Exposure Time (VMET) is computed to quantify the risk of exposure to the SARS-CoV-2 under various environmental and occupancy scenarios. Comparing the VMET under varying conditions should help identify viable methods to reduce the risk of viral exposure of CATS bus passengers during the COVID-19 pandemic.

Mitigation of voltage sags in distribution line system using static VAR compensator and static synchronous compensator

Voltage sag is one of the power quality disturbances most frequently by customers because it can cause economic loss for the customers especially industries and commercial customers. The main source of voltage sags are faults in transmission and distribution beside two others namely motor large starting and transformer energizing, both the voltage sags have less effect on the equipment. Voltage sags can cause degradation performance of the equipment, and it depends on the magnitude and the duration. The voltage sags that occur in the power system can be compensated with installed Static VAR compensator (SVC) and Static synchronous compensator (STATCOM) in the distribution system bus. The purpose of the study is to compare both types of the device used to mitigate voltage sags in simulation using Alternative transient program (ATP) software. The voltage sag results from the short circuit faults. The method proposes the installation of the two compensator devices alternatively at one of the IEEE thirteen busses systems. The result shows the STATCOM is better than the SVC to handle mitigation of voltage sags compared with the SVC.

Detection and Mitigation of Sensor and CAN Bus Attacks in Vehicle Anti-Lock Braking Systems

For a modern vehicle, if the sensor in a vehicle anti-lock braking system (ABS) or controller area network (CAN) bus is attacked during a brake process, the vehicle will lose driving direction control and the driver’s life will be highly threatened. However, current methods for detecting attacks are not sufficiently accurate, and no method can provide attack mitigation. To ensure vehicle ABS security, we propose an attack detection method to accurately detect both sensor attack (SA) and CAN bus attack in a vehicle ABS, and an attack mitigation strategy to mitigate their negative effects on the vehicle ABS. In our attack detection method, we build a vehicle state space equation that considers the real-time road friction coefficient to predict vehicle states (i.e., wheel speed and longitudinal brake force) with their previous values. Based on sets of historical measured vehicle states, we develop a search algorithm to find out attack changes (vehicle state changes because of attack) by minimizing errors between the predicted vehicle states and the measured vehicle states. In our attack mitigation strategy, attack changes are subtracted from the measured vehicle states to generate correct vehicle states for a vehicle ABS. We conducted the first real SA experiments to show how a magnet affects sensor readings. Our simulation results demonstrate that our attack detection method can detect SA and CAN bus attack more accurately compared with existing methods, and also that our attack mitigation strategy almost eliminates the attack’s effects on a vehicle ABS.

Modeling and Simulation of System Bus and Memory Collisions in Heterogeneous SoCs

A system simulator is proposed and developed, which can help to optimize design parameters and hence minimize the number of collisions. In order to search the optimal design parameter combination which meets the user requirement, the proposed simulator has some knobs: partitioning between software and hardware, scheduling the operations in the system, and memory merging, all of which can be adjusted to predict collisions and search the optimal architecture. Also, design parameters can be adjusted sequentially to cover all design options and estimate the predicted performance for each option. The proposed system simulator is evaluated with an example signal processing algorithm, orthogonal matching pursuit (OMP) algorithm. Performances of four cases of the OMP algorithm are predicted by the proposed simulator and in turn are compared with the actual performances on ZedBoard. The proposed simulator can predict the performance of heterogeneous systems on chips with under 5% error for all the candidate architectures for OMP while taking the system bus and memory conflicts into account. Moreover, the optimized heterogeneous SoC architecture for the OMP algorithm improves performance by up to 32% compared with the conventional CAG-based approach. The proposed simulator is verified that the proposed performance estimation algorithm is generally applicable to estimate the performance of any heterogeneous SoC architecture. For example, the estimation error is measured to be no more than 5.9% for the convolutional layers of CNNs and no more than 5.6% for the LDPC-coded MIMO-OFDM. In addition, the optimized heterogeneous SoC architecture improves performance by up to 48% for the third convolutional layer of AlexNet and 56% for the LDPC-coded MIMO-OFDM. Lastly, compared with the conventional simulation-based approaches, the proposed estimation algorithm provides a speedup of one to two orders of magnitudes. The source code is available on the GitHub repository: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SDL-KU/HetSoCopt</uri> .

Structural-Constrained Methods for the Identification of False Data Injection Attacks in Power Systems

Power system functionality is determined on the basis of the power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as financial losses, maintenance damage, and disruptions in electricity distribution. Classical bad data detection (BDD) methods, developed to ensure PSSE reliability, are unable to detect well-designed attacks, named unobservable false data injection (FDI) attacks. In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks, the identification of the attacked buses' locations, and PSSE under the presence of such attacks. The proposed methods are based on formulating structural, sparse constraints on both the attack and the system loads. First, we exploit these constraints in order to compose an appropriate model selection problem. Then, we develop the associated generalized information criterion (GIC) for this problem. However, for large networks, the GIC method's computational complexity grows exponentially with the network size. Thus, based on the proposed structural and sparse constraints, we develop two novel low-complexity methods for unobservable FDI attack identification: 1) a modification of the state-of-the-art orthogonal matching pursuit (OMP); and 2) a method that utilizes the graph Markovian property in power systems, i.e. the second-neighbor relationship between the power data at the system's buses. The methods' performance is evaluated on a IEEE-30 bus test case system.

Development of New Protection Scheme in DC Microgrid Using Wavelet Transform

The demand for a low voltage direct current (LVDC) microgrid is increasing by the increase of DC-based digital loads and renewable resources and the rapid development of power electronics technology. For the stable operation of an LVDC microgrid, it is necessary to develop a protection method. In this paper, the new protection scheme considering the fault section is proposed using wavelet transform (WT) in an LVDC microgrid. The fault sections are classified into DC side of the alternating current (AC)/DC converter, DC/DC converter connected to photovoltaic (PV) system, DC line, and DC bus. The characteristics of fault current at each fault section are analyzed. Based on these analyses, the new protection scheme including the fault section estimation is proposed using WT. The proposed scheme estimates the fault section using the detail component after performing WT and sends the trip signal to each circuit breaker according to the fault section. The proposed protection scheme is verified through various simulations according to the fault region and fault current using electromagnetic transient program (EMTP)/ATPDraw and MATLAB. The simulation results show that the fault section is accurately determined, and the corresponding circuit breaker (CB) operations are performed.

Available Technologies for Mass Transport Modes in Smart Cities

Mass transit is essential to the movement of people in urban cities. A city that intends to be smart needs to integrate mass transit systems—buses, tramways, subways, and trains—with Intelligent Transportation Systems (ITS) to improve urban transport services. The objective of this study is to analyze the relation of mass transportation modes in smart cities with the technology applied to them. Thus, a literature review was conducted to identify scientific publications about the theme, and the textual mechanic analysis using Iramuteq software 4.0.3 was applied. This study cites the technologies incorporated into mass transportation modes presented in literature and describes the current scenario of smart and sustainable mobility for public transportation.

A Missing Data Tolerant Wide-Area Back-Up Protection Scheme for Transmission Network

The proposed scheme develops a missing data tolerant wide area back-up protection scheme for power transmission network with fewer PMUs. The scheme assumes division of power system into different coherent groups. The fault detection index (FDI) is used to detect a fault in the system network using active power transfer and angular difference between voltages of connected areas or summation of negative and zero sequence current components in each area. The faulted area is then identified by change in area-current (CAC) using current contribution towards each area and finally, the faulted line is identified for the respective faulted area using distance to fault (DF) index. The scheme is extensively tested on a New-England 39-bus system for several fault and stressed scenarios considering all PMU data available and PMU data loss at some of the buses. For this, the PMUs are deployed at less than half number of system buses. The maximum response time of the proposed scheme is approximately 260 ms which is well within the third zone operation time of distance relays for back-up protection. The test results including the comparative assessment with existing schemes indicate that the proposed scheme can be a potential candidate for enhancing the performance of the wide-area back-up protection in the transmission network.

Control of Reactive Power with Genetic Algorithm in Electrical Power Systems with Photovoltaic Power Plant.

The present paper has as its objective to optimize the reactive power in electric systems that have a photovoltaic power plant connected to it, thus aiming at improving the voltage profile of all system buses, so that they meet the values determined in the standard. For this, it is proposed to accurately determine the ideal amount of reactive power, using the genetic algorithm, in which there is no use of approximate equations, reduction of the active power of the photovoltaic source and, moreover, it allows the regulation of the voltage of all the buses in the system, being able to raise or reduce their voltage level. The proposed methodology is validated through the analysis of the 14-bus electric system from IEEE, into which a photovoltaic power plant was connected. Studies were carried out with six different load scenarios in the literature to observe the performance of the proposed algorithm. Through the analysis of the results, one concludes that the developed genetic algorithm is efficient for determining the reactive power values that result in the reduction or increase of the voltage levels of all buses in the system, allowing them to meet the values determined in the regulatory standards.

Trusted Execution Environment Hardware by Isolated Heterogeneous Architecture for Key Scheduling

A Trusted Execution Environment (TEE) sets a platform to secure applications based on the Chain-of-Trust (CoT). The starting point of the CoT is called the Root-of-Trust (RoT). However, the RoT implementation often relies on obscurity and provides little flexibility when generating keys to the system. In this paper, a TEE System-on-a-Chip (SoC) architecture is proposed based on a heterogeneous design by combining 64-bit Linux-capable processors with a 32-bit Micro-Controller Unit (MCU). The TEE is built on the 64-bit cores, while the 32-bit MCU takes care of sensitive data and activities. The MCU is isolated from the TEE side by an Isolated Bus (IBus) that sits above the conventional System Bus (SBus). Besides the 32-bit processor, the isolated sub-system contains a Random Access Memory (RAM), a Read-Only Memory (ROM) for storing the boot program, and another ROM for storing root keys. For cryptography accelerators, we have 512-bit Secure Hashing Algorithm 3 (SHA3-512), 128/256-bit Advanced Encryption Standard (AES-128/256), Ed25519, and True Random Number Generator (TRNG) attached to the Peripheral Bus (PBus). Additionally, besides the public channel, the TRNG module also has a private channel that goes directly to the IBus. With RoT implemented inside the isolated sub-system, the RoT is inaccessible from the TEE side after boot. Furthermore, the hidden MCU’s secure boot program makes the key generation flexible and could be updated for many security schemes. To summarize, the proposed design features a flexible and secure boot procedure with complete isolation from the TEE domain. Moreover, exclusive secure storage for the root key and cryptographic accelerators are available for the boot process. The implementation was tested on a Virtex-7 XC7VX485T Field-Programmable-Gate-Array (FPGA). It was also synthesized in a Very Large-Scale Integrated (VLSI) circuit with the ROHM-180nm process library.

Approximate multi-objective optimization for integrated bus route design and service frequency setting

This paper focuses on the transit network design and frequency setting problem for bus transit systems, where solutions are simultaneously generated in favor of the passengers and operator. While multi-objective solutions exist in the literature, simplifications and inconsistent assumptions make them hard to compare. For example; researchers have proposed solutions that report on a minimal fleet size without considering the service frequencies that guide the passenger’s travel options, as a result, logical choices made by all passengers would cause the buses in the transit system to be overcrowded. To address this issue, we have developed a mathematical model which couples service frequencies and passenger assignment reflective of available choice options a passenger can take in the network. This approach can achieve lower operating costs while still ensuring that passengers can reach their destination at no detriment to journey times. We have constructed a complete set of multi-objective solutions, and compared them with those in the literature. The experiments demonstrate that our current solutions generally dominate those in the literature.

A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement

PurposeThis paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line.Design/methodology/approachAn efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network (“GW-NN”) to place the PMUs in power grids optimally.FindingsFollowing extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches.Practical implicationsThe efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems.Originality/valueThis paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases.

A Novel Reliability Index Based Approach for EV Charging Station Allocation in Distribution System

Large-scale deployment of electric vehicles (EVs) is deleterious for distribution system operations. Unplanned installation of EV charging station (EVCS) deteriorates the voltage stability, reliability, and power quality of the distribution system leading to customer discontentment. This article proposes a novel approach for the proper placement of EVCS in a distribution network maintaining the system performance. The design of a test station with vehicle-to-grid (V2G) capability is outlined in this work. The test station is developed with five charging columns taking into consideration the standards and guidelines outlined in the Government of India EV policy. The charging station is connected to the power grid by a dc bus to which numerous EVs are attached. EVCS placement in the IEEE-33 bus radial distribution system is performed by developing six test scenarios. The test case results substantiate the potency of the reliability-index based approach for allocating EVCS both slow and fast chargers in a distribution network. Further, the maximal threshold value for placing the EVCS in various buses in a distribution system is determined using a novel electric vehicle placement index (EVPI). The novelty of the approach lies in the fact that it can include all operational parameters influenced owing to EV deployment, thereby maintaining the overall system performance without any physical restructuring of the network. The efficacy of the approach and the severity level of EVCS placement are validated for a real-time distribution system in Kerala, India.

Aggregation of Radial Distribution System Bus with Volt-Var Control

The high penetration of the distributed energy resources (DERs) encourages themselves to implement grid-supporting functions, such as volt-var control. The quasi-static time-series (QSTS) simulation is an essential technique to evaluate the impact of active DERs on the grid. Meanwhile, the increase of complexity on the circuit model causes a heavy computational burden of QSTS simulation. Although circuit reduction methods have been proposed, there have been few methods that can appropriately handle the distribution system (DS) with multiple voltage control devices, such as DERs implementing volt-var control. To address the remaining issues, this paper proposes an offline bus aggregation method for DS with volt-var control. The method determines the volt-var curve for the aggregated bus on the basis of historical data to reduce error in the aggregated model, and its offline process solves the computational convergence issue concerned in the online one. The effectiveness of the proposed method is validated in the simulation using a Japanese low-voltage DS model. The simulation results show that the proposed method can reduce the voltage error and computational time. Furthermore, the versatility of the proposed method is verified to show the performance does not heavily depend on how to select historical data for model-building.

Protection Coordination Method Using Symmetrical Components in Loop Distribution System

Power utilities worldwide commonly use the radial distribution system because of its advantages of being simple in structure and having relatively inexpensive installation costs. It has a disadvantage in that its power supply reliability is low because the load side of the fault section will suffer from an outage in the event of a fault in the system. However, recently, with ICT (Information and Communication Technologies) development, system reliability is required to be high as the outage-susceptible loads increase. In addition, the increase in the connection of distributed resources such as renewable energy and electric vehicles is making it impossible to predict the power flow and reducing line utilization. Therefore, a loop power distribution system is proposed as a measure to solve this problem. Because all buses (nodes) in a loop distribution system have two or more power supply routes, they are more reliable than the radial system. It allows them to improve line utilization by connecting lines with different load peak times. However, in the case of a fault in the loop distribution system, the fault current is supplied from both directions, making it impossible to properly isolate the fault section with the protection method of the conventional distribution system. The permissive overreach transfer trip (POTT) method using communication to compensate for the limitations of conventional protection devices, and the other method using directional distance relay, is proposed. However, these methods operate by determining the direction of the fault current but have a disadvantage. It is difficult to detect a fault due to the effects of ground faults and distributed generation (DG) occurring in other lines. Therefore, in this paper, we propose a protection coordination algorithm that uses the negative-sequence component of voltage and current that occur when an unbalanced fault occurs, rather than the determination of the directionality and use of communication. To validate this, we configured a system using PSCAD/EMTDC (Manitoba Hydro International Ltd., Winnipeg, Manitoba, Canada), a system analysis program package and verified the results depending on the type of faults with the proposed algorithm.

An ideal solution for the deployment of photovoltaic generators using agent-based Nash Differential Evolution (NashDE) algorithm

Abstract This paper combines evolutionary game theory and Agent-based modelling for finding out the optimal location and size of multiple solar PVs in an unbalanced distribution system. The use of Agent-based modelling in solar PV placement is new and hence provides a valuable contribution. NashDE algorithm is implemented and simulated using an agent-based modelling framework MESA. In MA-NashDE, the optimization variables, i.e., the power system buses, are clustered into several zones using both the Loss Sensitivity Index and the Voltage Sensitivity Index. Each zone is under the control of a Nash/zonal player, and the strategy adopted by the zonal player will evolve through Multi-Agent Differential Evolution (MADE) algorithm. This is a novel study focusing on the PV placement problem by Agent-based Evolutionary Game Theory. The proposed methodology was tested using IEEE 34 and IEEE 123 bus radial distribution network using the OpenDSS-Python COM interface.

Statistical method for identification of weak nodes in power system based on voltage magnitude deviation

Identification of weak buses in an electric power system is important for operational and planning purposes. In this paper, a framework that uses the variance of the magnitude of voltage at load buses when reactive power of all loads are varied concurrently is used to identify a set of weak buses. In the framework, reactive power for all load buses are generated concurrently and independently based on the probability distribution function of load demands at each node. Three IEEE test systems are used to test the framework in Pypower in conjunction with Python programming language. The result shows that weak buses can be identified with the framework. It was observed that the first three set of most critical nodes in a network can be identified within a variation of 1% of reactive power at load nodes in case of ordinary power flow (pf), while the same set of nodes can be identified with at least 5% of variation of reactive power at nodes based on optimal power flow (opf). One area of application of this work could be in the use of the variance of voltage magnitude measurements to quickly identify a subset of weak nodes in a network.

Optimal open-circuit voltage (OCV) model for improved electric vehicle battery state-of-charge in V2G services

Electric vehicles (EVs) with voltage-to-grid (V2G) capability are useful in augmenting grid capability to handle high energy demand of end users during peak periods. We propose a hybrid state-of-charge (SOC) battery model with aggregator to optimize battery charging and maintain grid stability during peak periods. The proposed SOC model leverages the advantages of three well-known previously proposed battery models namely: Shepherd, Unnewehr and Nernst models. The proposed hybrid model is a combination of the merits of the three specified empirical Lithium-ion battery models to optimize slow charging. This will enhance battery performance by improving its depth-of-discharge profile. This results in enhanced V2G capability and longer driving time for EV owners. Battery parameters used in the simulation are for Nissan Leaf 2019 EV. The proposed SOC model parameters are used to optimize a two-objective function which is used by the aggregator to maximize benefits to both EV owners and DSO. Multi-objective genetic algorithm (MOGA) is used to optimize the objective function because of its ability to obtain non-dominated solutions while still maintaining diversity of the solutions. From simulation results, proposed OCV model improves battery SOC by 10% after V2G operating period (2 p.m.) compared to a case without the model. Also, proposed model earns aggregator $445 and $45 more for voltage and frequency regulation services, respectively. Voltage stability of all 5 considered grid buses of the IEEE 33-node system remains at 0.9–1.0 p.u.