Iet Generation Transmission & Distribution最新文献

In order to improve the measurement accuracy of mutual inductance parameters of transmission lines in the environment of double circuit power lines without power outage, this paper establishes simulation models for measuring mutual inductance parameters of transmission lines using the different frequency method in two modes: equal and unequal zero sequence self-parameters of two circuit lines, using a parallel zero sequence coupling model of double circuit power lines. In the article, simulation analysis is conducted on the line parameters with a coupling coefficient between 0.4 and 0.6 and a line length of 20–50 km. In order to further verify the correctness of the simulation model and measurement methods, a test bench was established based on the principle of line mutual inductance parameter testing in a laboratory dual circuit line without a power outage environment for experimental testing. By comparing the test values under experimental conditions with the standard values, it has been proven that the model and method can meet the actual measurement requirements of engineering.

This research addresses the pressing need for enhanced energy management in smart homes, motivated by the inefficiencies of current methods in balancing power usage optimization with user comfort. By integrating reinforcement learning and a unique column-and-constraint generation strategy, the study aims to fill this gap and offer a comprehensive solution. Furthermore, the increasing adoption of renewable energy sources like solar panels underscores the importance of developing advanced energy management techniques, driving the exploration of innovative approaches such as the one proposed herein. The constraint coordination game (CCG) method is designed to efficiently manage the power usage of each appliance, including the charging and discharging of the energy storage system. Additionally, a deep learning model, specifically a deep neural network, is employed to forecast indoor temperatures, which significantly influence the energy demands of the air conditioning system. The synergistic combination of the CCG method with deep learning-based indoor temperature forecasting promises significant reductions in homeowner energy expenses while maintaining optimal appliance performance and user satisfaction. Testing conducted in simulated environments demonstrates promising results, showcasing a 12% reduction in energy costs compared to conventional energy management strategies.

Given the complex form of distribution line faults, the accuracy of fault location using traditional artificial intelligence networks needs to be further improved. Here, a combined fault location method is proposed for a 110 kV distribution line based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), mantis search algorithm (MSA), and convolutional gate recurrent unit (ConvGRU). Firstly, the study used the ICEEMDAN algorithm to decompose the signals and discard the high-frequency signals with low correlation so as to achieve the purpose of noise cancellation. Then, the study used the root mean square error (RMSE) of the ConvGRU model training as the adaptation value, optimized the internal parameters of the model using the MSA algorithm, and obtained a combined fault locating model. By using the proposed model, the effects of the fault form and transition impedance changes on the location accuracy were analysed, and the location accuracy was compared with other artificial intelligence methods. The location accuracy index showed that the proposed model had a better convergence speed of training error than the traditional model. Also, the RMSE of the localization results was reduced by 50%, with a higher fault location accuracy.

Power system unreliability tracing model allocates the system's reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered as the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model's allocation results are reasonable and more accurate than the traditional model.

This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi-objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition-based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable-based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE-33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

This paper proposes an optimization approach for allocating power quality monitors (PQMs) aiming to monitor all harmonic resonance conditions while taking power system uncertainties into account. The placement approach utilizes the frequency scan response for calculating impedances over a range of frequencies and consequently determining harmonic resonance conditions. Thereby, it is capable of building binary matrices, which include harmonic resonance conditions. Also, by utilizing the union operator at binary matrices, power system uncertainties such as photovoltaic generation and load level can be considered in the allocation method. The placement approach is expressed as a linear problem that determines the best locations of PQMs and their optimal number so that they monitor all harmonic resonance conditions. Besides, by considering the area of harmonic pollution of non-linear loads in the proposed method, owners of electrical networks can find a solution with fewer PQMs to monitor harmonic resonance orders inside a particular area of the network. The performance of the presented approach is demonstrated using the 15-node distribution network and a real electrical network, as well as a real large electrical network in Iran. Results show that the proposed method suggests fewer PQMs to monitor harmonic resonance conditions compared to previous methods.

The active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability. However, the ADN is more vulnerable to cyber attacks due to the recent wave of digitization and automation efforts. In this article, false data injection (FDI) attacks are focused on and they are classified into two types, that is, type I attacks on measurement data and type II attacks on control commands. After studying the impact of these two FDI attacks on the ADN, a new threat is revealed called coordinated FDI attack, which can maximize the voltage deviation by coordinating type I and type II FDI attacks. From the attacker's perspective, the scheme of CFDI is proposed and an algorithm is developed to find the optimal attack strategy. The feasibility of CFDI attacks has been validated on a smart distribution testbed. Moreover, simulation results on an ADN benchmark have demonstrated that CFDI attacks could cause remarkable voltage deviation that may deteriorate the stability of the distribution network. Moreover, the impact of CFDI attacks is higher than pure type I or type II attacks. To mitigate the threat, some countermeasures against CFDI attacks are also proposed.

This paper presents various control strategies to improve operations in two interconnected areas connected by a VSC-HVDC transmission line. The main focus is on designing a central control system (CCS) that coordinates control units in both areas. In area 1, an AC voltage control unit is connected to the CCS. In area 2, three control units including a load power control unit, a fault detection unit, and an AC voltage control unit are also connected to the CCS. The CCS receives inputs from these units and generates commands for the DC voltage and active/reactive power control units on both sides of the DC line. The first proposed strategy addresses permanent voltage drops caused by load fluctuations in area 2. It adjusts the transmitted power from area 1 based on voltage variations in area 2. The second strategy focuses on mitigating faults in area 2 by injecting active and reactive power from area 1 during such events. The third strategy resolves transient voltage oscillations in both areas by controlling the reactive power of stations on either side of the DC line. Simulations using MATLAB-SIMULINK demonstrate that these mechanisms successfully achieve their objectives.

The paper presents a new method for estimating the contribution of distortion sources based on the application of passive harmonic filters. The method does not require the measurement of harmonic network impedance and is based on the measurement of harmonic currents of the grid, consumers and passive harmonic filters. Evaluation of the consumer contribution using a passive harmonic filter is necessary to select the parameters and connection points for harmonic reduction devices. A feature of the method is the correct determination of share contributions, regardless of background harmonic distortions. Based on this method, a single consumer can evaluate both the harmonic contributions of its own loads, even in the presence of background distortions, and the harmonic voltage distortions in the case of installing harmonic reduction devices. The research results are confirmed in laboratory conditions with various combinations of electrical loads connected both at the grid side and at the consumer side. For such conditions, the proposed method was compared with existing methods, among which are methods based on the measurement of harmonic voltage vectors, harmonic current vectors and active harmonic power. The application of the developed method was demonstrated using the example of a gas production field.

In power systems, voltage collapse during overload can be a significant threat. Accurate forecasting of critical operational conditions within power grids is crucial for preventing such situations. Precise predictions of voltage collapse enable operators to monitor the system closely and implement necessary corrective measures promptly, avoiding potential issues. However, monitoring networks can be costly due to the numerous loads and transformers in the distribution system. A comprehensive approach known as the voltage stability index (VSI) forecast without measurement buses (VFWMB) has been introduced to address this challenge. This approach involves innovative methods, including the seeking observation zone with weight least square (SOZWLS) technique for determining the number and location of measurements in the network based on its topology. Additionally, short-term load forecasting is performed using the long short-term memory (LSTM) forecasting method, followed by voltage estimation for buses without measurements. Finally, the proposed method calculates the modern voltage stability index for distribution systems (MVSIDS) for upcoming hours. All indicators and techniques in the VFWMB method have been validated. The algorithm has been thoroughly tested on various networks, including small and large, balanced and unbalanced, and both real and test networks, showing high efficiency in the electricity industry.