Control Power System Research Articles

A Controllable Response Power Beam Localization Method and System Implementation

Sound source localization technology has gradually become one of the main methods for fault source target localization in dangerous and difficult-to-discover applications. In order to locate the target fault sound source more intuitively, accurately and in real time in practical applications, an audio-visual positioning system is developed. A 64-channel microphone array is designed, which is evenly distributed in a circular shape, and has a video acquisition target at the same time. The system uses GCC-PHAT to estimate the relative time delay between the signal source arriving at two microphones, and then uses SRP-PHAT algorithm to obtain weighted sum beamforming. A spatial grid search strategy based on spherical coordinate spatial contraction method is proposed. When the output power is maximum, the direction corresponding to the beam is regarded as the sound source direction. Using the same sound source signal, the proposed positioning method is compared with the direct Newton iterative method and the Cartesian coordinate scanning SRP-based maximum method. The simulation results show that the Cartesian coordinate value and spherical coordinate value of the positioning algorithm in this article are closer to the real coordinates of the sound source position than the other two methods, and the positioning operation time is the shortest. In addition, in the actual corona discharge power source positioning experiment, the system can accurately locate the insulator pollution discharge point, which is consistent with the infrared detection and positioning results and has good robustness. Therefore, the system location method can provide a new choice for sound source location applications.

Security concern and fuzzy output sliding mode load frequency control of power systems

This research investigates the concern of security in multi-area load frequency control (LFC) power systems using fuzzy logic output sliding mode control approach. A motion-trends-based deception attack model is proposed to represent malicious threats from communication networks. The motion-trends-based deception attack model enhances the destructive effect of deception attacks by monitoring changes in system state. In terms of the security concerns, a fuzzy-based output sliding mode control (OSMC) is applied to protect the power system from potential cyber-attacks. We provide sufficient conditions for the power systems stability under motion-trends-based deception attack. An initial controller gain that will bring the power system to stability is calculated by linear matrix inequalities (LMIs). The obtained controller gain provides a design method for fuzzy logic member functions. Fuzzy logic is then used to adjust the resulting controller gain to improve power system performance. The simulation results validate the usefulness of the proposed strategy, and indicate the viability of fuzzy logic in fine-tuning controllers.

Power Systems and Power Electronics using the DEMATEL Method

Energy and research on energy systems places a focus on all facets of electrical energy, as well as innovation in energy production and delivery, alternative resources, and efficient devices. Systems and equipment for converting, providing, and utilising energy as a kind of electricity are the subject of research initiatives. Power electronics are now a more integral part of power systems, enhancing quality and efficiency and fostering the gradual materialization of intelligent, efficient energy. Many different types of power electronics exist in power systems. The architectural study of changing electrical transformed from one medium to another is known as power electronics. Fields such as electronics reprocess or recover more than 80percent of the entire of the electricity produced at a world average rate of 3.4 billion kilowatts per hour each year. Power electronics converters, sometimes referred to as power converters or switching converters, are used to process or convert electrical energy. Electricity comes in two flavours: AC power and DC power. The distribution system is divided into AC distribution systems and DC distribution systems based on the type of power it uses. Power system analysis is an essential part of electrical power system design. Calculations and simulations are performed to verify that the electrical system, including the system components, is properly specified to perform as planned, withstand the expected stress, and be protected from failures. Advantages of Power Electronics: High power density electricity. Improved efficiency up to 99% in energy conversion. Because to its efficiency and dependability, switching power supply are also being used in medical devices with acoustically sensitive applications. Power system reliability, in general, relates to problems like service interruption and power outages. This is frequently described as a goal to try relying on codes specifically relevant to the consumer. SAIFI, SAIDI, and CAIDI are common dependability index values for US applications. DEMATEL (Decision Making Trial and Evaluation Laboratory) They are divided into analysis using the Nonmetal mineral product industry, General equipment manufacturing, Mining and washing of coal, Textile industry, Food manufacturing industry It is the interaction between the factors Visualized and assesses dependent relationships Through the structural model Also deals with identifying important. Power Electronics in Power Systems, Power Electronics in Transportation Systems, Energy Conservation, Heating & Lighting Control and Renewable Energy Integration Power Systems and Power Electronics in Power Electronics in Power Systems is got the first rank whereas is the Energy Conservation is having the Lowest rank. Power Systems and Power Electronics in Power Electronics in Power Systems is got the first rank whereas is the Energy Conservation is having the Lowest rank.

Stability and Stabilization for T–S Fuzzy Load Frequency Control Power System With Energy Storage System

Stability and Stabilization for T–S Fuzzy Load Frequency Control Power System With Energy Storage System

A Bayesian Deep Learning-Based Probabilistic Risk Assessment and Early-Warning Model for Power Systems Considering Meteorological Conditions

The ongoing process of decarbonizing the power system and the frequent occurrence of extreme weather have led to a significant increase in operating uncertainty and greatly reduced the system controllability. An effective risk assessment and early-warning tool will significantly assist system operators in monitoring and controlling power systems. However, existing methods mainly rely on simplified analytical conditional probability models to describe the component failure probability under different operating conditions and are not suitable for low-probability risk assessment. Inspired by the idea of Bayesian statistics, a Bayesian deep learning-based probabilistic risk assessment and early-warning (BDL-PRAEW) model considering meteorological conditions is proposed in this paper. A new Bayesian neural network (BNN) is proposed which efficiently utilizes expert experience and domain knowledge as prior to model the contingency probability. And a hybrid neural network is developed to rationally utilize the multi-source heterogeneous data and comprehensively analyze the historical and forecast information. Finally, a novel risk assessment and early-warning model for high-impact, low-probability (HILP) extreme events is proposed, which can predict the risk for the next n time-steps. The proposed method is tested on a modified IEEE 118-bus system and a real-world provincial grid, and the results verify its effectiveness.

Predictor-Based Load Frequency Control for Large-Scale Networked Control Power Systems

Predictor-Based Load Frequency Control for Large-Scale Networked Control Power Systems

Data-driven assessment of center of inertia and regional inertia content considering load contribution

The inertia displacement in power grids, caused by the massive integration of renewable energy resources (RES) and responsive loads, has become one of the biggest challenges to operating and controlling power systems. Locating the centre of inertia (COI) of a region can help identify this inertia displacement. This work proposes a novel fully data-driven disturbance-based methodology to estimate both the COI and the inertia of a region, where the variation of the COI due to RES and load contribution are considered. The methodology uses a recursive form of the typicality-based data analysis (TDA) to find the pilot-bus (TDAp). The TDA methodology approximates the multi-modal distribution of active power and frequency measurements by the typicality property, to detect the COI of each region and the corresponding pilot-bus. A composite metric of correlation and cosine similarities of active power and frequency measurements is used to approximate the measurements’ unknown distribution. The window of measurements necessary for detection is attained by the convergence of the typicality variance. The frequency of the detected pilot-bus of the region and tie-lines active powers deviations are used by an auto-regressive moving average exogenous input (ARMAX) approach to estimate the inertia of a regional equivalent machine. The methodology is capable of identifying RES and load contribution, since the pilot-bus detection through the TDAp is sensible to the COI displacement by these equipments, unlike traditional methods that only consider an average of synchronous machines’ inertia and some heuristics for load contribution. The proposed methodology is tested by using the IEEE 68-bus benchmark test system, an adapted version with aggregated dynamical loads, and also with RES participation through type-3 wind generators, corroborating the effectiveness of the proposal.

Stability characteristics analysis and ultra-low frequency oscillation suppression strategy for FSC-VSPSU

The research on control strategies of the variable speed pumped storage unit with full size converter (FSC-VSPSU), mainly focuses on the rapidity of power regulation. But the speed oscillation and slow speed regulation performance caused by fast power regulation are ignored, which is not conducive to the safe and efficient operation of the unit. This article investigates the dynamic stability characteristics of the FSC-VSPSU in detail. In turn, the stability risks are clarified and serve for the design of its control strategy and the improvement of operational efficiency. The small-signal model of FSC-VSPSU considering the variation of water head and load in fast power mode is established. The dominant instability control link and key parameters are revealed based on the eigenvalue analysis. Oscillation mode analysis shows that the unit has a wide-band oscillation characteristic. The dominant oscillation mode is the ultra-low frequency oscillation (ULFO) caused by the governor control system. To suppress the ULFO and improve the optimal speed tracking performance of the unit to the greatest extent, a coordinated control strategy of governor variable parameter control and governor power system stabilizer (GPSS) is proposed. The electromagnetic transient simulation based on PSCAD verifies the effectiveness of the proposed measures.

Frequency regulation scheme in power system using an observer-based waiting event-triggered control with random features

Addressed in this paper is the observer-based waiting event-triggered control issue in multi-area load frequency control (LFC) power system subject to random communication delay and data packet dropout. In order to maintain the grid frequency stability of the multi-area LFC power system in an open communication network, an observer-based waiting event-triggered scheme (WETS) is proposed to deal with frequency fluctuation and extend the life of control equipments. Compared with the existing event-triggered mechanism, the proposed WETS has the merits of continuous event-triggered scheme and the periodic sampling event-triggered scheme in the both of theory and application. Then, by well considering the negative factors of data packet dropout and random time delay in data transmission, a stochastic delay-dependent multi-area LFC model is established, which involves the information of observer-based WETS, random communication delay and packet dropout in a unified structure. Further, an improved Lyapunov–Krasovskii functional (LKF) is constructed by dividing the interval of transmitted delay. Based on the constructed Lyapunov functional, Park’s theorem and Jensen’s integral inequality, some less conservative stabilization criteria are obtained. Finally, the validity of the proposed control method is verified via numerical examples.

Blockchain-based cyber-security enhancement of cyber–physical power system through symmetric encryption mechanism

The increasing dependence on the communication network in monitoring, operating, and controlling power systems increases the threat of cyber-attacks. Cyber attacks could harm the power system’s functioning and cause substantial economic losses. Malicious injection of compromised synchrophasor measurements is capable of hampering adequate control actions and jeopardizing the security and reliability of the power system. To tackle this issue, an adequate method for detecting cyber intrusion in the communication network should be developed for the secure operation of the cyber–physical power system. Blockchain has evolved as a proven technology that holds the potential for enhancing grid security. In this paper, we investigate the application of blockchain for enhancing grid security and propose a random sequence-based symmetric encryption algorithm that uses a blockchain-based secure platform for sharing the decryption keys. The presence of cyber-attack has been identified using a weighted least square-based state estimator. We have further developed a cyber-attack simulator application that could emulate the behavior of a false data injector and proposed the attack detection mechanism. The developed technology has been tested on IEEE 14 bus test systems.

Multi-Machine Power System Transient Stability Enhancement Utilizing a Fractional Order-Based Nonlinear Stabilizer

Given the intricate nature of contemporary energy systems, addressing the control and stability analysis of these systems necessitates the consideration of highly large-scale models. Transient stability analysis stands as a crucial challenge in enhancing energy system efficiency. Power System Stabilizers (PSSs), integrated within excitation control for synchronous generators, offer a cost-effective means to bolster power systems’ stability and reliability. In this study, we propose an enhanced nonlinear control strategy based on synergetic control theory for PSSs. This strategy aims to mitigate electromechanical oscillations and rectify the limitations associated with linear approximations within large-scale energy systems that incorporate thyristor-controlled series capacitors (TCSCs). To dynamically adjust the coefficients of the nonlinear controller, we employ the Fractional Order Fish Migration Optimization (FOFMO) algorithm, rooted in fractional calculus (FC) theory. The FOFMO algorithm adapts by updating position and velocity within fractional-order structures. To assess the effectiveness of the improved controller, comprehensive numerical simulations are conducted. Initially, we examine its performance in a single machine connected to the infinite bus (SMIB) power system under various fault conditions. Subsequently, we extend the application of the proposed nonlinear stabilizer to a two-area, four-machine power system. Our numerical results reveal highly promising advancements in both control accuracy and the dynamic characteristics of controlled power systems.

Quenching chaos in a power system using fixed-time fractional-order sliding mode controller

The aim of this paper is to study the unwanted chaotic oscillation that can severely affect the reliable and safe operation of electrical power systems. The dynamical behavior of a benchmark three-bus nonlinear electrical power system model is explored using modern nonlinear analysis methods, where the Lyapunov exponents spectrum, bifurcation diagram, power spectral density and bicoherence are used to investigate the chaotic oscillation in the power system. The analysis shows the existence of critical parameter values that may drive the power system to an unstable region and can expose the system to bus voltage collapse and angle divergence or blackout. To eliminate the chaotic oscillation, a fractional-order fixed time sliding mode controller has been used to control the power system in a finite time that can be predetermined by the designer. The Lyapunov theorem has been used to prove the stability of the controlled power system. The results confirm the superiority, robustness, and effectiveness of the suggested control algorithm.

Research on Application Scenarios of Artificial Intelligence in New Power System

Building a New Power System with new energy as the main body is the main theme of modern energy system construction and energy for a period of time in the future. It is an inevitable choice to promote the clean and low-carbon development of electric power. Through artificial intelligence technology, we can solve the problems of strong volatility and difficult regulation caused by the diversity of new energy sources, and promote the establishment of a green, efficient, flexible, interactive, safe and controllable New Power System. This paper sorts out the challenges and deficiencies faced by New Power System, summarizes the application characteristics of artificial intelligence in various industries, analyzes the development and characteristics of artificial intelligence applications, combines the application hotspots of artificial intelligence New Power System, studies the application scenarios of artificial intelligence in the construction, operation and maintenance of New Power System, and puts forward suggestions on the research direction.

Controlled Power System Separation Using Generator PMU Data and System Kinetic Energy

With the growing number of severe system disturbances and blackouts around the world, controlled system separation is becoming an increasingly important system integrity protection scheme (SIPS) to save the electric power system from a complete or partial disintegration. A successful controlled splitting approach should at least tackle the following two well-known and interrelated problems: "when to split?" and "where to split?". Multiple previous publications consider these problems separately, and even those pursuing a combined approach propose solutions with limited applicability. In this paper, we are proposing a novel PMU-based detector of loss of synchronism that utilizes generator rotor angles and speeds to promptly detect rotor angle instabilities over a wide area. Moreover, we are showing how our loss of synchronism detection principle can be coupled with the known controlled splitting techniques to form an integrated defense scheme against unintentional loss of synchronism. The performance of this wide-area SIPS is demonstrated on the IEEE 39-bus test power system for various types of unstable conditions.

Influence of ultra‐capacitor on AGC of five-area hybrid power system with multi-type generations utilizing sine cosine adopted dingo optimization algorithm

In this article, the load frequency control (LFC) problem of a five-area automatic generation control (AGC) power system with multi-type generations is addressed by utilising a technique that considers the changing of the dingo optimisation algorithm known as sine cosine adopted dingo optimisation algorithm (SCaDOA) based tilt integral derivative (TID) regulator. AGC needs an energy storage system (ESS) and some intelligent adaptable control techniques to guarantee the balance in the system's stability. Consequently, this paper utilizes the TID regulator alongside ultra‐capacitor (UC) ESS to settle the AGC issue. At first, the execution of the sine cosine adopted calculation is tried by contrasting it and the standard DOA calculation by considering different standard benchmark functions. Further, the proposed plan strategy is likewise applied to a five-area system comprising different conventional and non-conventional/renewable energy sources along with UC. The execution of the controller has been finished for different stacking conditions of the power system. It is seen that the said UC with TID controller is more viable contrasted with the other standard controllers.

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations.

This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.

Solar-PV inverter for the overall stability of power systems with intelligent MPPT control of DC-link capacitor voltage

This paper demonstrates the controlling abilities of a large PV-farm as a Solar-PV inverter for mitigating the chaotic electrical, electromechanical, and torsional oscillations including Subsynchronous resonance in a turbogenerator-based power system. The oscillations include deviations in the machine speed, rotor angle, voltage fluctuations (leading to voltage collapse), and torsional modes. During the night with no solar power generation, the PV-plant switches to PV-STATCOM mode and works as a Solar-PV inverter at its full capacity to attenuate the oscillations. During full sun in the daytime, on any fault detection, the PV-plant responds instantly and stops generating power to work as a Solar-PV inverter. The PV-farm operates in the same mode until the oscillations are fully alleviated. This paper manifests the control of the DC-link capacitor voltage of the Solar-PV inverter with a bacterial foraging optimization-based intelligent maximum power point tracking controller for the optimal control of active and reactive power. Kundur’s multi-machine model aggregated with PV-plant is modeled in the Matlab/Simulink environment to examine the rotor swing deviations with associated shaft segments. The results for different test cases of interest demonstrate the positive outcomes of deploying large PV-farms as a smart PV-STATCOM for controlling power system oscillations.

Stability assessment using adaptive interval type-2 fuzzy sliding mode controlled power system stabilizer

Stability assessment using adaptive interval type-2 fuzzy sliding mode controlled power system stabilizer

On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data

The problem of state estimation of a linear, dynamical state-space system where the output is subject to quantization is challenging and important in different areas of research, such as control systems, communications, and power systems. There are a number of methods and algorithms to deal with this state estimation problem. However, there is no consensus in the control and estimation community on (1) which methods are more suitable for a particular application and why, and (2) how these methods compare in terms of accuracy, computational cost, and user friendliness. In this paper, we provide a comprehensive overview of the state-of-the-art algorithms to deal with state estimation subject to quantized measurements, and an exhaustive comparison among them. The comparison analysis is performed in terms of the accuracy of the state estimation, dimensionality issues, hyperparameter selection, user friendliness, and computational cost. We consider classical approaches and a new development in the literature to obtain the filtering and smoothing distributions of the state conditioned to quantized data. The classical approaches include the extended Kalman filter/smoother, the quantized Kalman filter/smoother, the unscented Kalman filter/smoother, and the sequential Monte Carlo sampling method, also called particle filter/smoother, with its most relevant variants. We also consider a new approach based on the Gaussian sum filter/smoother. Extensive numerical simulations—including a practical application—are presented in order to analyze the accuracy of the state estimation and the computational cost.

Empirical study of the effect of the air filter on the performance and exhaust emissions of a diesel engine

The results of an experimental study of the effect of the pressure drop of the air filter pf on the operat-ing parameters and exhaust emissions of a modern CI internal combustion engine of a truck equipped with an electronically controlled power system are presented. The tests were carried out for an air filter with a clean filter cartridge Δpf0 = 0,58 kPa and with a cartridge contaminated after a service mileage (about 50 thousand km) ΔpfD = 2,024 kPa. In each test, engine performance, exhaust emissions and relative change in emissions were determined: CO, NOx, HC, CO2, H2O. It was found that an increase in the filter resistance pf causes a decrease in the filling degree by 12%, engine useful power by almost 10%, exhaust gas temperature by a maximum of 30oC and an increase in specific fuel consumption by almost 5%. Air filter resistance has no significant effect on NOx emissions and HC concentration. There is a reduction in H2O emissions by up to 7%, CO by up to 13% and CO2 by up to 4%, and an increase in oxygen emissions by 15%, depending on operating conditions.