Standard Bus Research Articles

False data injection attack detection in dynamic power grid: A recurrent neural network-based method

The smart grid greatly facilitates the transmission of power and information by integrating precise measurement technology and efficient decision support systems. However, deep integration of cyber and physical information entails multiple challenges to grid operation. False data injection attacks can directly interfere with the results of state estimation, which can cause the grid regulator to make wrong decisions and thus poses a huge threat to the stability and security of grid operation. To address this issue, we propose a detection approach against false data injection attacks for dynamic state estimation. The Kalman filter is used to dynamically estimate the state values from IEEE standard bus systems. A long short-term memory (LSTM) network is utilized to extract the sequential observations from states at multiple time steps. In addition, we transform the attack detection problem into supervised learning problem and propose a deep neural network-based detection approach to identify attacks. We evaluate the effectiveness of the proposed detection approach in multiple IEEE standard bus systems. The simulation results demonstrate that the proposed detection approach outperforms benchmarks in improving the detection accuracy of malicious attacks.

Design considerations and applications of a biomedical test bench development kit

Abstract Applications and requirements for biomedical test benches are often quite different, but there is always a need to monitor and usually to control processes. Commercially available development systems can be used to build simple laboratory setups up to industrial systems but are limited with respect to high costs and information for required validation. A test bench development kit is presented which ensures high compatibility for prospective developments, adaptations and embedding of sensors and actuators. The development kit is based on a printed circuit board built around the singleboard computer Raspberry PI. Supporting circuitry for power control, logics and special functions are implemented. Several types of peripheral devices can be connected via two serial bus standards (I2C, or RS-485). Current applications demonstrate the achieved level of automation and process control.

Minimization of crosstalk noise and delay using reduced graphene nano ribbon (GNR) interconnect

In this paper, we proposed a side-contact reduced graphene nano-ribbon (SC-RGNR) interconnect model to reduce crosstalk noise and delay for next generation high performance integrated circuit (IC) design. The SC-RGNR interconnect structure is designed with latest 11 nm ITRS (International Technological Road Map for Semiconductor) technology node. A comparative crosstalk noise, delay and power consumption analysis has been performed with side-contact graphene nano-ribbon (SC-GNR) interconnect model. The SC-RGNR and SC-GNR interconnect both model parameters are applied in standard (STD) bus and staggered (STAGG) bus model with different interconnect lengths (10 μm–500 μm) and four standard mean free paths (MFP) (i.e., 300 nm, 419 nm, 1000 nm and 1200 nm) values. Five different switching conditions (i.e., SP1 to SP5) are applied on tri-interconnect driver-load circuit to check the performance of SC-RGNR and SC-GNR interconnect in-terms of crosstalk noise and delay. In our analysis, it is shown that, the higher MFP based SC-RGNR interconnect shows less dynamic delay compared with SC-GNR interconnect for both STD and STAGG interconnect model with worst-case switching condition (SP5) and best-case switching condition (SP2). For STD interconnect model with worst-case switching condition (SP5) the dynamic delay of SC-RGNR interconnect is ∼1.33–2.62 × lesser than SC-GNR interconnect. Similarly, for STAGG model with worst-case switching condition (SP5) the dynamic delay of SC-RGNR interconnect is ∼1.26–2.16 × lesser than SC-GNR interconnect. Although, the STAGG interconnect model performs ∼1.5–2 × faster than STD interconnect model in terms of delay. In our analysis, it is also observed that, the output noise peak is ∼2.91–17.5 × less in SC-RGNR compared with SC-GNR at 500 μm interconnect length and higher MFP. Our analysis also shows that, the switching power consumption in SC-RGNR is ∼1.66–2.2 × less than SC-GNR for both STD and STAGG model for both best and worst-case switching condition (SP2 and SP5).

Real-Time Locational Detection of Stealthy False Data Injection Attack in Smart Grid: Using Multivariate-Based Multi-Label Classification Approach

Recently, false data injection attacks (FDIAs) have been identified as a significant category of cyber-attacks targeting smart grids’ state estimation and monitoring systems. These cyber-attacks aim to mislead control system operations by compromising the readings of various smart grid meters. The real-time and precise locational identification of FDIAs is crucial for smart grid security and reliability. This paper proposes a multivariate-based multi-label locational detection (MMLD) mechanism to detect the presence and locations of FDIAs in real-time measurements with precise locational detection accuracy. The proposed architecture is a parallel structure that concatenates Long Short-Term Memory (LSTM) with Temporal Convolutional Neural Network (TCN). The proposed architecture is trained using Keras with Tensorflow libraries, and its performance is verified using an IEEE standard bus system in the MATPOWER package. Extensive testing has shown that the proposed approach effectively improves the presence-detection accuracy for locating stealthy FDIAs in small and large systems under various attack conditions. In addition, this work provides a customized loss function for handling the class imbalance problem. Simulation results reveal that our MMLD technique has a modest advantage in some aspects. First, our mechanism outperforms benchmark models because the problem is formulated as a multivariate-based multi-label classification problem. Second, it needs fewer iterations for training and reaching the optimal model. More specifically, our approach is less complex and more scalable than benchmark algorithms.

Design of a 32-Bit, Dual Pipeline Superscalar RISC-V Processor on FPGA: A Review

Abstract: A 40 MHz with 32-bit and 5-stage dual-pipeline superscalar processor based on RISC-V Instruction Set Architecture is presented. It supports integer, multiply-divide and atomic read-modify-write operations. The proposed system implements inorder issuing of instructions. The design includes a dynamic branch prediction unit, memory subsystem with virtual memory, separate instruction cache and data cache, integer and floating-point execution units, interrupt controller, error control module, and a UART peripheral. Individual interrupts can have up to four levels of primary priority set in the interrupt controller. For the main memory, the error control module provides single error correction and double error detection. For on-chip communication, the Wishbone B.3 bus standard is used. On a Virtex-7 XC7VX485T FFG 1761-2 FPGA-based board, the processor is implemented. The architecture has CoreMark and Dhrystone benchmark ratings of 3.84/MHz and 1.0603 DMIPS/MHz, respectively

Statistical analysis based reactive power optimization using improved differential evolutionary algorithm

AbstractThis study presents a novel improved differential evolutionary (IDE) algorithm for optimizing reactive power management (RPM) problems. The effectiveness of IDE algorithm is tested on different unimodal and multimodal benchmark functions. The objective function of the RPM is considered as the minimization of active power losses. Initially, the power flow analysis approach is employed to detect the optimal position of flexible AC transmission system (FACTS) devices. The proposed method is used to determine the optimal value of control variables such as generator's reactive power generation, transformer tap settings, and reactive power sources. Furthermore, the efficacy of the IDE approach is compared with other promising optimization methods such as variants of differential evolution algorithm, moth flame optimization (MFO), brainstorm‐based optimization algorithm (BSOA), and particle swarm optimization (PSO) on various IEEE standard test bus (i.e., IEEE‐30, ‐57, ‐118, and ‐300) systems with active and reactive loading incorporating FACTS devices. A Static VAR compensator (SVC) for shunt compensation and a thyristor‐controlled series compensator (TCSC) for series compensation were used as FACTS devices. The proposed IDE method significantly reduces the active power loss, that is, 55.65% in IEEE 30, 39.68% in IEEE 57, 16.32% in IEEE 118, and 8.56% in IEEE 300 bus system at nominal loading. Finally, the statistical analysis such as Wilcoxon signed‐rank test (WSRT) and ANOVA test were thoroughly analysed to demonstrate the firmness and accuracy of the proposed technique.

The Optimal Integration of Multiple DGs under Different Load Models using Artificial Bee Colony-Hill Climbing Algorithm

With the continual advances in technologies, there is a significantly increased demand for electricity leads to a drastic increase in usage and penetration of DGs into the distribution network. The incorrect addition of DGs into the distribution system resulting in declining stability, power losses, voltage profile, reliability, and power quality of the system. Therefore, the optimal planning of DGs (OPDG) has always been under constant investigation and development for the maximum beneficiary. The proposed framework presents a novel meta-heuristic hybrid optimization technique called Artificial Bee Colony-Hill Climbing (ABC-HC) to find the best siting and sizing of multiple DGs using a weighted multi-objective function which includes real and reactive power loss index and voltage deviation index. The results clearly indicate the significant reduction in losses and voltage deviation achieved by integration of single and multiple DGs. The research work is also extended to assess the voltage harmonics of the system before and after the placement of DGs. The effectiveness of the proposed ABC-HC algorithm has been demonstrated on the standard IEEE-33 bus RDS under different voltage dependent load models using MATLAB 2017a.
 HIGHLIGHTS
 
 As demand grows, technologies advance, and power system infrastructure changes, there are more opportunities to integrate distributed generators into the distribution system
 The maximum benefit from the DG integration is achieved by selecting the optimal size and optimal site in the distribution system
 A new hybrid optimization technique has been developed for optimal planning of DGs under different voltage dependent loads. The work was also extended to carry out harmonic analysis
 
 GRAPHICAL ABSTRACT

Multi-Parametric Investigations on Aerodynamic Force, Aeroacoustic, and Engine Energy Utilizations Based Development of Intercity Bus Associates with Various Drag Reduction Techniques through Advanced Engineering Approaches

The impacts of conflicting aerodynamic forces and side drifting forces are the primary unstable elements in automobiles. The action of an unstable environment in automobile vehicles increases the chance of an accident occurring. As a result, much study is required to determine how opposing aerodynamic forces and side drifting force affects function, as well as how to deal with them for safe and smooth navigation. In this work, an intercity bus is chosen as a main object, and computational fluid dynamics (CFD) analysis is used to estimate aerodynamic forces on the bus in all major directions. Experimentation is also carried out for validation reasons. CFD findings for a scaled base model and a dimple-loaded model based on experimental results from a subsonic wind tunnel are demonstrated to be correct. The drag forces generated by CFD simulations on test models are carefully compared to the experimental drag findings of same-dimensioned models. The error percentages between the results of these two methods are acquired and the percentages are determined to be within an acceptable range of significant limitations. Following these validations, CATIA is used to create a total of nine distinct models, the first of which is a standard intercity bus, whereas the other eight models are fitted with drag reduction techniques such as dimples, riblets, and fins on the surface of their upper cumulus side. A sophisticated computational tool, ANSYS Fluent 17.2, is used to estimate the comparative assessments of the predictions of aerodynamic force fluctuations on bus models. Finally, dimples on the top and side surfaces of the bus model (DESIGN–I) are proposed as a more efficient model than other models because dimples are a vital component that may lower pressure drag on the bus by 18% in the main flow direction and up to 43% in the sideslip direction. Furthermore, by minimizing the different aerodynamic force sources without impacting the preparatory needs, the proposed model may provide comfortable travel. The real-time bus is created, and the finalized drag reduction is applied to the optimized places over the whole bus model. In addition, five distinct size-based bus models are developed and studied in terms of aerodynamic forces, necessary energy to resist aerodynamic drag, required forward force for successful movement, instantaneous demand for particular power, and fuel consumption rate. Finally, the formation of aeroacoustic noise owing to turbulence is estimated using sophisticated computer simulation. Last, for real-time applications, multi-parametric studies based on appropriate intercity buses are established.

An Environment Friendly Energy-Saving Dispatch Using Mixed Integer Linear Programming Relaxation in the Smart Grid with Renewable Energy Sources

Electrical energy demand has risen over the world which results in developmentof smart grid. Smart grid has identified the areas that requiresimprovement. Because of the focus on cost-effective operation as well asenvironmental concerns in the electrical power system, the complexity ofthe optimization function has increased. In this study, a new energy-saving dispatch model is developed, which takes into account renewable energysources in the smart grid as well as dynamic generation-load interaction.Moreover, the active demand response idea is employed for interruptibleloads during peak demand. During off-peak load periods and compensatingloads, the proposed energy-saving dispatch system operates on a bi-leveldispatch system. Lower level dispatch works with four dispatch functionssuch as interruptible load cost, compensation load cost, renewable energysource cost, and emission saving cost. Whereas, upper level dispatch dealswith cost functions for operation and emission. Renewable energy sources arerepresented as a generating unit as well as a load based on usage in this work.Linear programming relaxation and mixed integer linear programming relaxationmethodologies are used to solve the constrained optimization problem.The outcomes of the experiments were compared with existing methodologiessuch as the classic NSGA-II and the improved NSGA-II. Furthermore,the algorithm’s time complexity was examined. The proposed solution hasbeen implemented using the IEEE-30 bus standard. The performance resultsdemonstrate considerable reductions in operating costs as well as reductionsin emissions.

Salp swarm algorithm for solving optimal power flow problem with thyristor-controlled series capacitor

In this paper, a salp swarm algorithm (SSA) is proposed for solving the optimal power flow (OPF) problem of a power system with the incorporation of the thyristor-controlled series capacitor (TCSC). The proposed methodology is implemented for determining the optimal setting of control variables for the OPF problem, which includes the real power of generators buses, voltages of generator buses, reactive power injected by shunt compensators, and tap changing transformer ratios. The performance of the proposed approach is validated and tested on the standard IEEE-30 bus system and single-objective functions, including transmission line losses. The severity factor has been minimized and the result obtained is compared with the existing algorithms. Simulation results achieved with the proposed SSA approach demonstrate that it results in an effective and better solution for the OPF problem.

Design of Aeronautical Digital Video and Communication Bus Processing System

Abstract With the development of avionics systems, Avionics Digital Video Bus (ARINC 818) and Digital Information Transmission System (Arinc429) have become a new generation of aviation video transmission and communication bus standard respectively. Aiming at the characteristics of ARINC 818 and Arinc429, an aeronautical digital video and communication bus processing system is designed. First, the ARINC 818 aviation video transmission bus and the Arinc429 communication bus standard are introduced. Then, the design principles of the aeronautical digital video and communication bus processing system are described according to this standard, and the design scheme of each system component is introduced in detail. Finally, build a hardware test platform for testing and verification. The system development follows an integrated and modular design, with the Application Processing Unit (APU) main processor and Field Programmable Gate Array (FPGA) as the core, which realizes the superposition of 1-channel input ARINC 818 video with internally generated characters, and then outputs 1-channel ARINC 818 to the function displayed on the display. At the same time, it supports 7-channel Electronic Industries Association-422 (EIA-422) and 4-channel Arinc429 bus communication. In addition, the system supports the processing of 22 input discrete quantities and 7 input analog quantities. The design has been successfully used in a certain type of aircraft display control management system.

Optimal Cumulative Cost Strategy for the Thermal-Solar Integrated Radial Distribution System with Minimized Carbon Footprint for Sustainable Solution for Clean Energy

ABSTRACT The article presents optimal cost approach for the solar- thermal integrated radial distribution system. The gap between supply and demand can be bridged by integration with solar photovoltaic systems to existing networks. Such integration consequently increases size of power system network components and enhances cost of the system. The cumulative cost of system consists of thermal emission cost, thermal fuel cost, direct cost of solar generation while emission cost of solar is indirectly contributed in the form of raw material extraction, transportation, manufacturing of solar photovoltaic cell etc. The carbon emission releases its mark into atmosphere as a carbon footprint. In order to overcome such issue, an optimal complex non -linear multi-objective is framed separately for component size, thermal generating cost and solar generation direct cost while another multi-objective function represents the solar indirect cost and carbon footprint which is represented as loss of carbon emission which comprises three components: loss of cost of energy, loss of power supply probability and cost constraint. The multi-objectives function has been minimised with the proposed power exponential method and genetic algorithm. The reliability and effectiveness of the proposed methodology has been tested successfully on standard IEEE-30 bus system. It is observed that optimal thermal fuel cost, thermal emission cost, total thermal generating cost, solar direct and indirect cost and thereby total solar generating cost and finally cumulative cost of system, plus carbon footprint estimation are found to be lesser with proposed method in comparison with genetic algorithm and existing techniques.

An efficient multi-objective based squirrel search optimization for optimal placement of FACTS devices

The adoption of a new transmission line is extremely complex because of its socio-economic problems such as environmental clearances. Thus, there is a prominence of better utility over available transmission infrastructure. The Flexible Alternating Current Transmission System (FACTS) devices can offer transmission capability enhancement, power compensation, and stability as well as voltage improvement. However, the FACTS devices have a higher penetration impact of wind generation for the dynamic stability of power networks. In this work, an efficient Intellectual Control system has been proposed to stabilize the FACTS devices placement. The Squirrel Search Optimization is adapted with an intellectual control system to enhance the steady-state voltage stability of FACTS devices. The proposed system has been evaluated with the assist of IEEE 14 and 26 standard bus systems to handle the multi-objective functions like cost, reduction in power loss, reducing risks, and maximizing user’s benefit. These multi-objective functions facilitate to attain the optimal placement and load flows at various sites. The simulation can be carried out with MATLAB/SIMULINK environment and the results manifest that the proposed system outperforms well when compared with existing approaches.

Energy harvesting maximization by integration of distributed generation based on economic benefits

The purpose of distributed generation systems (DGS) is to enhance the distribution system (DS) performance to be better known with its benefits in the power sector as installing distributed generation (DG) units into the DS can introduce economic, environmental and technical benefits. Those benefits can be obtained if the DG units' site and size is properly determined. The aim of this paper is studying and reviewing the effect of connecting DG units in the DS on transmission efficiency, reactive power loss and voltage deviation in addition to the economical point of view and considering the interest and inflation rate. Whale optimization algorithm (WOA) is introduced to find the best solution to the distributed generation penetration problem in the DS. The result of WOA is compared with the genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The proposed solutions methodologies have been tested using MATLAB software on IEEE 33 standard bus system.

An alternative test method for the dual-port SRAM through the DesignWare SATA AHCI implementation in 3D IC structures

The conventional methods for testing the Dual Port Random Access Memories (DPRAM) may not be suitable for the Three-Dimensional Integrated Circuit (3D IC) structures, due to their limited test vectors with heterogeneous integration. This paper takes an Application Specific Integrated Chips (ASIC) approach to gain some insight into the Front-end behavioral testing and backend routability of the dual port memories with complex Serial Advanced Technology Attachment (Serial-ATA) design, in a 3D IC structure. The presented implementation used commercially available Dual Port memories from the two different vendors. The commercially available DesignWare Advanced Host Controller Interface (SATA/AHCI), which is based on the SATA 2.6 AHCI host and external SATA (eSATA) standard bus architecture, with My_foundry’s 14 nm low power process (14lp) technologies for the logical and physical implementations. The methodology evaluates the memory quality and performance/characteristics, in terms of timing, power and area. This paper shows memory testing in both implementation/verification in a readily built-up SATA test environment.

Study of optimization techniques for optimal location of facts devices

The methods used for finding the best value out of the bulk amount of data available in a sample are known as optimization techniques. Different types of such techniques are used to make machines more efficient. FACTS devices play a vital role to compensate for the reactive power of the power system by means of improving the voltage profile of the system, as the reactive power of a power transmission system directly depends upon the magnitude of the voltage. The Newton-Raphson (NR) load flow method is used for IEEE 14 bus with its standard bus and branch data. The optimal size and location of G-UPFC found out by using ant colony optimization (ACO) is inserted in the different IEEE bus systems.

Optimum generation and VAr scheduling on a multi-objective framework using exchange market algorithm

This paper presents an application of exchange market algorithm (EMA) in solving multi-objective optimisation problems of power systems. This optimisation algorithm is based on the activities of shareholders to maximise their profit in the exchange market. The uniqueness of this algorithm lies in the fact that, it enjoys double exploitation and exploration property unlike several other algorithms. In order to investigate its search capability, the EMA is utilised to solve power systems active and reactive related objectives simultaneously in presence of several nonlinear constraints. Both optimum generation and VAr planning problems are formulated as conventional optimal power flow (OPF) problem. Fuel cost (active related objective), transmission line loss and total voltage deviation (reactive related objectives) are taken as different objective functions. The multi-objective optimisation problem is performed through weighted sum approach. Both fuzzy and equal weight approach is utilised to declare the compromised solution. Programs are developed on MATLAB and simulations are performed on Standard IEEE-30 and IEEE-57 bus systems. The search capability of EMA in solving the multi-objective power system problems are compared with PSO based solutions.

Application of the AERMOD Model to Evaluate the Health Benefits Due to Air Pollution from the Public Transport Sector in Ha Noi, Viet Nam

Fine particulate matter (PM2.5) mainly originates from combustion emissions on-road transportation. Exposure to PM2.5 could be considered one of the primary causes of diseases such as heart attack, stroke, lung cancer, and chronic respiratory, which made it one of the most important co-benefits when evaluating the impact of GHG mitigation measures. This study quantifies the co-benefit of Ha Noi’s modal shift from private to public means of transport, which are reduced air pollution and extended life expectancy, combining AERMOD model and benefit transfer method. Analytical results show that shifting from motorbike to electric train could be the most beneficial option in term of health co-benefit, compared to the usage of standard buses and BRTs.

SENECAN: Secure KEy DistributioN OvEr CAN Through Watermarking and Jamming

The Control Area Network (CAN) represents the standard bus for intra-vehicular networks communication. Unfortunately, CAN was not designed to be a secure protocol. Communications over CAN do not take advantage of any security feature (e.g., cryptography and authentication), raising different vulnerabilities in critical applications. This lack of security is even more emphasized in recent CAN networks, which integrate remote connection capabilities (e.g., Bluetooth and WiFi). This insecurity-by-design led to the development of specific mechanisms to patch CAN vulnerabilities. Many proposed solutions rely on implementing optimized cryptographic primitives and assume that the cryptographic keys were previously shared among the different nodes during the production phase, omitting the issue related to keys distribution and update. We propose SENECAN, a solution that combines watermarking and wired jamming to secure the CAN bus's key distribution. Our solution leverages intentional interference and spread spectrum watermarking to achieve security properties such as confidentiality, integrity, authentication, and anti-replay. Compared to other works, SENECAN does not require any modification of the CAN protocol and system architecture. Instead, it requires an additional CAN transceiver and an initial transmission overhead. Finally, we tested the effectiveness and functioning of the SENECAN distribution schema in a real CAN environment.

Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

Solar energy is the radiant heat and light energy harvested by ultra violet rays to convert into electrical Direct Current (DC). The solar energy stood ahead of other renewable energy as it can produce a constant level of alternating current over the year with minimal harmonic distortions. The renewable energy attracts the energy harvesters as there is rise of deficiency of carbon and reduction of efficiency in thermal energy generation. The concerns associated with the solar power generation are the fluctuation in the generated direct current due to the displacement of sun and deviation in the quantity of solar rays from place to place. This apprehension is overcome by following the technical methods of employing latest technology is determining the optimal position to harvest the solar power at the high rate and forecasting the power generation effectively. This paper proposes a novel hybrid methodology of employing fuzzy based controller to determine the Maximum Power Point Tracking (MPPT) in solar power generation and employing Artificial Intelligence (AI) technology to perform high precision forecasting of power generation. The K-Nearest Neighbor algorithm is a least assumption algorithm is employed in predicting the energy level harvested in the solar Photovoltaic cells. The Artificial Intelligence considers the vital parameters of displacement direction of the sun, temperature, clearness index and humidity in the air. The performance analysis of the proposed methodology is compared with the IEEE standard bus and the prediction is proved to be more precision with a maximum standard deviation of 0.06%.