Stable Operation Of Power System Research Articles

A Comprehensive Review of the Cyber-Attacks and Cyber-Security on Load Frequency Control of Power Systems

Power systems are complex systems that have great importance to socio-economic development due to the fact that the entire world relies on the electric network power supply for day-to-day life. Therefore, for the stable operation of power systems, several protection and control techniques are necessary. The power system controllers should have the ability to maintain power system stability. Three important quantities that should be effectively controlled to maintain the stability of power systems are frequency, rotor angle, and voltage. The voltage control in power systems maintains the voltage and reactive power within the required limits and the power factor control enhances the efficiency of power distribution systems by improving load power factors. Among various controls, the frequency control is the most time-consuming control mechanism of power systems due to the involvement of mechanical parts. As the control algorithms of frequency stabilization deliver control signals in the timescale of seconds, load frequency control (LFC) systems cannot handle complicated data validation algorithms, making them more vulnerable to disturbances and cyber-attacks. In addition, the LFC system has extended digital layers with open communication networks and is designed to operate with less human intervention. Moreover, the frequency fluctuation due to load change or cyber-attack in one area affects all other interconnected areas, and thus threatens the stability of the entire network. Due to these circumstances, research activities are still carried out in the field of frequency control and cyber-security. In this paper, a comprehensive review of the cyber-security of the LFC mechanism in the power system is presented. The highlights of the paper include the identification of attack points of different configurations of the LFC system, discussion of the attack strategies, formulation of various attack models, and a brief review of the existing detection and defense mechanisms against cyber-attacks on LFC.

Credibility‐based secure distributed load frequency control for power systems under false data injection attacks

In the distributed load frequency control (DLFC) scheme, the compromised areas by attackers may endanger the stable and reliable operation of the entire power system. A credibility-based secure DLFC strategy is in this work proposed to sustain the stable operation of the power system under false data injection attacks. In this secure DLFC method, the gradientvariation- based credibility metrics are defined to detect potential attacked measurements. Then, the communication isolation decisions are made based on the adaptive thresholds. To compensate the impact of the communication isolation of misbehaving areas, predictions of future measurements by using the average gradient variation of stored healthy data are feeded back to the area controller. Simulation results of a four-area power system show the proposed secure DLFC can identify and cut off the communication of the misbehaving areas and still maintain the stability of the power system.

Economic feasibility of a wind-battery system in the electricity market with the fluctuation penalty

As the rapid development of wind energy and electricity markets, wind farms (WFs) in mounting numbers join the electricity markets such as spot and regulating markets, etc. The large-scale integration of WFs into the power system may threaten the safe and stable operation of power systems due to the inherent fluctuation nature of wind power, and as a consequence, the WFs may be penalized by the grid operator in the electricity market. In this case, WF owners will take actions to reduce the fluctuation and one of the promising strategies is to integrate battery storage system (BSS) into the WFs. Base on this idea, a novel framework of considering fluctuation penalty (FP) in the Danish spot market which is the electricity market investigated in this study is firstly proposed. The economic feasibility of integrating BSS into a WF in the spot market is analyzed and a bi-level programming is adopted to design the sizing of the BSS based on Return on Investment (ROI). This research also analyzes the impact of various fluctuation penalties and subsidies on the ROI. It is found that the net revenue of integrating BSS into a WF can increase by 15.8% and the FP can reduce by 64.9% in the spot market with the FP.

Gain Schedule PI Fuzzy Load Frequency Control for Two-Area Electric Power System

The use of proportional integral (PI) load frequency control (LFC) to ensure the stable and reliable operation of electric power system is practical important. Any imbalance between synchronous generators and consumption loads will cause frequency unstable within the complete power system. The purpose of the load frequency control (LFC) is to keep the power system frequency and the inter-area tie power as near equilibrium point. This article introduces a gain schedule PI fuzzy load frequency control (GLFC) applying to two area electric power system. The GLFC consists of two level control systems, where the PI controller in the conventional form and its parameters are tuned in real time by fuzzy system. A fuzzy rule base is constructed in the form set of IF-THEN that describe how to choose the PI parameters under different operating conditions. The simulation has been conducted in MATLAB Simulink package. The effectiveness of the GLFC is measured by comparison with conventional PI load frequency controller.

Inverter-Data-Driven Second-Level Power Forecasting for Photovoltaic Power Plant

Globally, the installed capacity of photovoltaic (PV) power plants is undergoing rapid growth. However, the random output power fluctuation of PV plants has brought great challenge to power system stable operation. One of the most effective approaches to counteract the impact of power fluctuation is to carry out power forecasting under ultra-short-term scales, among which the second-level power forecasting technique is still absent since the pure historical data analysis of PV plant output power cannot deal with the sudden output power change induced by moving cloud and the traditional cloud image detection methods actually fail to achieve accurate PV power forecasting in high temporal and spatial resolutions. This article proposes an inverter-data-driven method to achieve the second-level PV power forecasting. In specific, multilayer feed-forward artificial neural network based on the error back propagation algorithm is applied to first establish the mapping relations from shading conditions of PV array to its corresponding output power. Combining with output power of neighboring PV arrays as well as their geographical location information, the shading conditions of those shaded PV array can be reversely deduced. And then, the deduced shading conditions of all PV arrays will be converted into one virtual cloud image to identify the instant cloud characteristics. In consequence, the consecutively generated virtual cloud images can depict the moving direction, speed, and shape of shading cloud and then help easily realize the second-level power forecasting of PV plant. Case study and experimental results verified the performance of the proposed method.

송전철탑 및 전력설비 자동검출을 위한 영상 분석 알고리즘 개발

Over the past few decades the capacity of the power transmission system is growing more and more rapidly, and the power systems have also been expanding in line with continuous expansion of infrastructure. Efficient maintenance of the transmission infrastructure is critical for stable power system operation. However, since the transmission infrastructure is installed at a high location from the ground, the maintenance and diagnosis on transmission towers always involves the possibility of the safety accident of workers. Since 2016, Korea Electric Power Corporation has been developing technologies for monitoring and diagnosis of power lines using drones and has verified the usefulness of technologies through on-site demonstration. 3GBytes of video will be acquired by one flight. And video analysis is performed by below process. ① A worker finds an image frame that includes a transmission tower and power facilities, ② and expands the area of interest in the image, ③ finally the visually observes the facilities and determines whether defects are. These process requires the concentration of the operator and causes very high fatigue. To solve these problems, it is necessary to develop an image analysis algorithm for automatic detection of transmission towers and power facilities to automatically find image frames containing facilities and automatically enlarge the area of interest in the frame to provide diagnosis operator. In this paper describes the process of building a learning and testing image database for detecting transmission towers and facilities carried out for the development of these algorithms and the process of developing algorithms using deep learning CNN-YOLO v2(Convolutional Neural Network - You Only Look Once v2) and the improvement of performance aspects including accuracy and speed with existing algorithms. The recall and precision rate of YOLO v2 for detecting transmission infrastructure is 96.30% and 95.65% respectively, which is more accurate and faster than YOLO v1.

Deterministic and probabilistic multi-step forecasting for short-term wind speed based on secondary decomposition and a deep learning method

The stochastic and intermittent nature of wind speed brings rigorous challenges to the safe and stable operation of power system. Wind speed forecasting is crucial for availably dispatching the wind power resource. In this paper the proposed model based on secondary decomposition (SD) and bidirectional gated recurrent unit (BiGRU) can accommodate long-range dependency and extract the semantic information of raw data. In the model, the GRU method is improved in directional nature. A second layer is added in GRU network to connect the two reverse and separate hidden layers to the same output layer. The PSR-BiGRU model of each subsequence is established and chicken swarm optimization (CSO) algorithm is employed to jointly optimize the parameters. The proposed method focuses on deterministic and probabilistic forecasting and does not involve any distribution assumption of the prediction errors needed in most existing forecasting methods. The effectiveness and advancement of the proposed model is tested by using data from two different wind farms. Comparing with other hybrid models, the proposed hybrid model is suitable for wind speed forecasting and could obtain better forecasting performance.

Short-Term Load Forecasting Based on Deep Neural Networks Using LSTM Layer

Short-term load forecasting (STLF) is essential for power system operation. STLF based on deep neural network using LSTM layer is proposed. In order to apply the forecasting method to STLF, the input features are separated into historical and prediction data. Historical data are input to long short-term memory (LSTM) layer to model the relationships between past observed data. The outputs of the LSTM layer are incorporated with outputs of fully-connected layer in which prediction data, for instance weather information for forecasting day, are input. The optimal parameters of the proposed forecasting method are selected following several experiment. The proposed method is expected to contribute to stable power system operation by providing a precise load forecasting.

Simulation Application and Research of Relay Protection Project under Virtual Reality Technology

The accurate and fast action of relay protection devices is an important guarantee for the safe and stable operation of power systems. In order to overcome the limitations of traditional training, this paper builds a simulation system of relay protection for substations based on virtual reality technology, so that trainees cannot only pass the man-machine Interactive operation to understand the appearance and basic operation of the equipment intuitively, and to check the action of the relay protection equipment under different faults through fault settings. Practical application shows that the system is of great significance to improve the quality of staff and ensure the safe and stable operation of the power system.

Research on Life Prediction of IGBT Devices Based on Elman Neural Network Model

IGBT, as a new generation of composite full-controlled voltage-driven power semiconductor devices, has been widely used in the field of modern power electronics. The study of IGBT device life prediction has important guiding significance for the stable operation and reliability management of power system. In this paper, the Cofin-Manson-Arrhenius extended exponential model based on thermal load is analyzed, and the model parameters are trained and modified by the Elman neural network in order to improve the prediction accuracy of life prediction model. The Coffin-Manson-Arrhenius extension exponential model and the new improved model are simulated and validated by experiments. The results are compared with the actual life of IGBT. It is concluded that the Coffin-Manson-Arrhenius extension exponential model based on Elman neural network has higher accuracy in life prediction than the Coffin-Manson-Arrhenius extension exponential model.

Dual possibilistic regression models of support vector machines and application in power load forecasting

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

Finite-time function projective synchronization control method for chaotic wind power systems

Wind power is a rapidly growing renewable energy source that plays an increasingly important role in power systems. However, its dynamic behaviors are complex because of the instability of wind speed, strong coupling, highly nonlinear subsystems, and mass grid connections. Chaotic oscillation is one of the most typical complex dynamic behaviors of wind power systems. This behavior can seriously influence the stable operation of wind power systems and cause great harm. This study proposed a control method for finite-time function projective synchronization on the basis of the chaos synchronization principle and finite-time theory for wind power systems. Wind power systems with or without uncertain parameters were considered. First, we built a high-dimensional wind power system and analyzed its chaotic behaviors. Lyapunov exponents were derived to prove the existence of chaos and bifurcation. Second, we aimed to increase the robustness of the controller by adding parameter observers to the controller. The result helped solve the problem of unknown parameters. If a wind power system comprises unknown parameters, the unknown parameters can be defined as the system's states. An unknown parameter observer was designed to realize the identification of unknown parameters. Third, we proposed a control method for finite-time function projective synchronization. Finite-time stability theory and function projective synchronization theory were used to construct the controller. These theories can ensure the quick synchronization of a chaotic wind power system with a stable wind power system in finite time. Finally, the mathematical proof of the stability theorem was derived, and a corresponding numerical simulation was performed to validate the chaos control method for wind power systems.

INVERSE DEFINITE MINIMUM TIME RELAY COORDINATION IN RADIAL TRACTION SYSTEM

For stable and reliable operation of power system, the coordination of relays plays an important role. In order to achieve fast and selective protection, the relays are to be coordinated through appropriate relay settings with correct grading margins. Relay settings are to be done at upstream and downstream in such a way that proper coordination is achieved along various series networks. This paper presents different factors which play major part in selection of Inverse Definite Minimum Time (IDMT) over current protection on radial network. The selection of IDMT curves and procedure applied in relay settings such as Plug setting multiplier (PSM), Time settings multiplier (TSM), grading margins and different techniques used in relay coordination in a traction system are discussed.

Flexibility requirement for large-scale renewable energy integration in Indian power system: Technology, policy and modeling options

Reliable and stable power system operation requires flexibility, in addition to capacity adequacy. Traditional system components either have limited flexibility to suppress extensive system variation, or their role is limited due to lack of proper regulatory provisions and inefficient market design. Large-scale integration of renewable energy (RE) resources (e.g., solar, wind) imposes additional variability and uncertainty to the existing system and thus enhances flexibility need. There are various solutions to the problem. Revamping system operation protocol with existing resources, retrofitting current power-generating assets, network expansion, etc. can provide flexible service. Investing in a new type of resources like energy storage and demand-side response (DSR) however, needs aggressive policy interventions and market mechanisms. Identifying suitable flexible resources and designing appropriate policy structures require long-term system planning. Traditional methods in this regard need to evolve to consider the operation-scale impact of large-scale RE integration at the planning stage such that long-term carbon emission reduction targets can be met. The approach towards transitioning into a flexible energy system can differ according to its present status. This paper focuses on the Indian power sector’s perspective, which has ambitious RE integration goals. With a fast-evolving system configuration, flexibility related challenges are high in India due to weak infrastructure, inefficient regulatory policies, and aggregated planning methods. This article presents the current status of the Indian power system detailing existing technology types, regulatory norms, and future targets. It further details a comprehensive review of relevant technical options, market mechanisms and planning approaches for transitioning into a flexible power system for India. Comparative analysis with international experiences highlights the need for a major paradigm shift. A short-term transition towards becoming a flexible system can focus on developing adequate transmission infrastructure, exploit available pumped hydro storage potential and retrofitting existing coal-fired power plants. On longer-term, innovating market mechanisms and regulatory changes should drive investment into emerging flexible resources like DSR, storage, etc. Planning for this transition should be designed using improved modeling and planning approaches which should focus on the interlinking of different sector-specific models.

An Oscillation Energy Calculation Method Suitable for the Disturbance Source Location of Generator Control Systems

Low frequency power oscillation threatens the secure and stable operation of power systems. Since the appearance of low frequency oscillation there are lots of approaches to analyze this problem. The oscillation source location method based on oscillation energy is adopted to find the cause of the oscillation, and to locate the oscillation source. Forced power oscillation is a kind of low frequency oscillation based on resonance mechanism, and the key is to quickly locate and remove the disturbance source. Aiming at the shortcomings of the existing disturbance source location methods, an oscillation energy calculation method suitable for the generator control systems level location is proposed. With the rich acquisition of generator data by means of phasor measurement unit (PMU), this method has the advantage of not relying on generator parameters. Taking the forced power oscillation caused by the abnormality of the governor system as an example, the superiority of the method is verified.

Control of the Variable-Speed Pumped Storage Unit-Wind Integrated System

The integration of variable-speed pumped storage unit (VS-PSU) guarantees an efficient peak regulation and frequency modulation of the power grid. The present research analyzes the active power flow of the VS-PSU under synchronous, subsynchronous, and supersynchronous speed in both generation and pumping modes. The control strategy of the VS-PSU is realized by the dq-axis vector control method. Furthermore, the control of the VS-PSU integrated with wind power has been conducted using DIgSILENT platform with the VS-PSU of 300 kW capacity. Results show that the fluctuation of the power output of the wind power plant can be reduced effectively. The safe, economical, and stable operation of power system integrated with wind power can be achieved by the significant contribution of the VS-PSU.

An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

An Efficient Summer Operation Scheme for a Thermal Power Plant Air Cooling System based on Electric Peak Shaving

We proposed a novel efficient operation scheme for a thermal power plant's air-cooling system based on peak shaving, in order to cope with high ambient temperature in summer. We introduced an absorption-generation equipment with water/lithium working pairs into the air cooled condenser (ACC) to reconstruct the traditional thermal power plant, and established a dynamic thermodynamic model adopting Ebsilon code. We studied the thermodynamic performance variation of the reconstructed thermal power plant throughout a 24-hour cycle and found that the fluctuation ratio of the turbine back pressure decreased to 6% from 78%, which is beneficial for the stable and safe operation of the electric power system. The thermal performance improvement benefited from the exploitation of the heat transfer potential of ACC, which realized via cold duty schedule throughout the day, under different ambient temperature conditions. In this system, the generated power was higher at relatively high ambient temperature than that at relatively low ambient temperature, which solved the electricity demand-supply imbalance problem under high ambient temperature. Finally, the same optimization effects for power thermal plants with an indirect air-cooling system were obtained using the same operation scheme.

Enhancement of Power Transfer Capacity and Transmission Efficiency using SSSC

The power demand is increasing globally at a higher rate than the possible increase in the generation. The increased demand requierements put additional burdon on the existing transmission lines and sometimes burdoned beyond their power carrying capacities. Increase in power demand either due modernization of power system, industrialization leads to congestion problem and abruptly affects the stable and reliable operation of power system. Redesign and reconstruction of power system according to load requirements everytime is not an economical and viable solution. Other possible solution to the problem is use of FACTS devices. The use of FACTS devices the problems like increase in load demand, high losses in transmission line and dip in receiving end voltage can be eliminated or easily tackeled. In this paper, Static Synchronous Series Compensator as one of FACTS device has been used for improvement of voltage profile of different buses and power carrying capacity of the transmission lines. The main objective of this paper is to make a comparative investigation between compensated and uncompensated power system in terms of enhancement of power carrying capacity with low losses and improvement of voltage profiles of buses in the transmission network. The performance of uncompensated power system has been compared with compensated power system with the use of MATLAB/ Simulink software.

A Methodological Framework to support Load Forecast Error Assessment in Local Energy Markets

Due to the expansion of small-scale distributed generation, residential consumers are evolving to active participants in energy markets. Concepts like Local Energy Markets (LEM) are designed to harvest flexibility of these prosumers and contribute to a stable power system operation. However, the stochastic nature of the consumption of households increases the difficulty of accurate forecasts and can lead to erroneous bids and penalty payments. State of the art load forecasting methods can reduce this error to a certain extend. Yet, for a systematic assessment of the implications of forecast errors, a method capable of generating forecast time series with defined errors is required. With this method, measures to decrease the implications of forecast errors (e.g., aggregation of participants) can be evaluated. In this paper, we introduce such a method based on nonlinear optimization. After an analysis of typically used error metrics and achieved forecast errors in the literature, the proposed method is evaluated using German household load profiles demonstrating similar statistical properties as found in the literature. Additionally, we show the application of the method to a LEM simulation case revealing that a participation of a household without flexible assets would only be profitable for forecast errors instead of accuracies below 30-40%.