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Air brake systems are crucial systems for safe and stable operation of electric multiple units (EMUs). The brake cylinder system, which includes brake cylinders, corresponding pressure sensors, and connection pipes, plays a vital role in the EMU air brake system. This is because brake cylinder pressures directly affect the brake operation. Currently, brake cylinder pressures are monitored by univariate control charts, i.e., the brake cylinder system will be considered as faulty if a certain pressure goes beyond its allowed range. Besides, serious sensor hardware faults such as open circuit or short circuit can also be detected by system self-inspection circuits. However, three kinds of faults, including brake cylinder component fault, soft sensor fault, as well as gas leakage fault, cannot be well handled by current monitoring methods if these faults are not very serious. In this paper, a fault detection index called intervariable variance (IVV) is first presented to perform fault detection for these faults. Fault detectability analysis is provided, and the IVV statistic is also compared with the univariate control chart approach. Then, a fault isolation strategy is proposed to distinguish different kinds of faults and determine the location of the occurred fault. Finally, the effectiveness of the proposed fault detection and isolation method is demonstrated via extensive experimental studies that are carried out on the EMU brake test bench of Qingdao Sifang Rolling Stock Research Institute Co., Ltd., China.

Detecting early abnormalities in blast furnaces is important for the smooth operation of the iron-making process. In this paper, recursive transformed component statistical analysis (RTCSA)-based algorithms are developed to monitor the iron-making process with the task of early abnormality detection. The increments of variables instead of the absolute measurements are used for RTCSA, in order to decrease the effect of the time-varying nature of the process. Owing to the peak-like disturbances caused by the switching of hot blast stoves, an online identification algorithm is designed to locate the disturbance intervals. Then an index-switching scheme is used for monitoring the process. The effectiveness of the proposed method is verified using the real data of two blast furnaces. Compared with the conventional methods such as the two-stage principal component analysis, the increment-based RTCSA can effectively detect early abnormalities in the iron-making process.

Accurate detection and isolation of an incipient fault is crucial for efficient and optimal operation of industrial processes. In the area of statistical process monitoring (SPM), reconstruction-based methods are widely used for fault isolation purposes. Though possessing several merits, most existing conventional reconstruction methods are not efficient in isolating incipient faults that have small magnitudes. This paper proposes a new method called exponential smoothing reconstruction (ESR) for incipient fault isolation. The proposed ESR introduces the exponentially weighted moving average (EWMA) technique to conventional reconstruction methods. Fault isolability analysis of the ESR is provided, which demonstrates its efficiency in isolating incipient faults, compared with conventional reconstruction-based methods. Several remarks on the use of ESR, such as selection of the smoothing parameter and some practical issues, are discussed as well. Finally, simulation studies on a numerical example and a cont...

This paper considers the isolation problem of incipient sensor fault. Based on recursive transformed component statistical analysis (RTCSA), two different isolation methods are proposed. The first method is called subspace reconstruction, where elements in specific subspaces are eliminated, and then reconstructed by minimizing the reconstructed detection index. The faulty variable is determined by the least scaled reconstructed detection index. The second method is called subblock detection, which has less online computational complexity. The subblocks of the measurement matrix are sequentially selected in each sliding window to calculate the subblock detection indices, and the faulty variable is determined by the largest subblock detection margin. Compared with the existing isolation methods such as reconstruction-based contribution (RBC) and its variant termed as average residual-difference reconstruction contribution plot (ARdR-CP), the superior isolation performances of the proposed methods are illustrated by a numerical example as well as a simulation on a continuous stirred tank reactor.

To investigate potential predictors of implant failure following fixation of proximal femoral fractures with a fracture of the lateral femoral wall.Medical records of 99 adult patients who had operative treatment for a proximal femoral fracture with a fracture of the lateral femoral wall between May 2004 and April 2015 were retrospectively analysed to determine factors associated with implant failure. Patients underwent routine surgical procedures for implantation of extramedullary or intramedullary devices. Potential predictors were age, gender, body mass index, comorbidities, type of fracture, reduction method, status of greater and lesser trochanters, course of the lateral fracture line, and presence/absence of a free bone fragment at the junction of the greater trochanter and lateral femoral wall.Ten (10%) implant failures were identified. Univariate analysis identified a free bone fragment at the junction of the greater trochanter and lateral femoral wall (odds ratio [OR], 21.25; 95% confidence interval [CI], 4.31-104.67; p 0.001) and a transverse fracture line across the lateral femoral wall (primary or iatrogenic) (OR, 5.36; 95% CI, 1.29-22.30; p = 0.021) as factors associated with implant failure. Using a multivariate model, only a free bone fragment at the junction of the greater trochanter and lateral femoral wall (OR, 16.05; 95% CI, 3.06-84.23; p = 0.001) was a risk factor for implant failure.A free bone fragment at the junction of the greater trochanter and lateral femoral wall and a transverse fracture line across the lateral femoral wall are predictors of implant failure in proximal femoral fractures with a fracture of the lateral femoral wall. Integrity of the lateral femoral wall correlates with prognosis of proximal femoral fracture. Lateral femoral wall reconstruction may be required for effective treatment of proximal femoral fractures with a fracture of the lateral femoral wall.

Planetary gearbox is one of the most important components of rotating machinery and plays a key role in modern industry. Due to the complex physical structures and harsh working conditions, planetary gearbox often suffers from different fault types, so it is of vital importance to investigate its fault diagnosis task. In this paper, a novel feature selection strategy is proposed to improve the multiclass support vector data description (SVDD) algorithm for planetary gearbox fault diagnosis. First, a novel feature selection method based on the cosine similarity measure in kernel space of Gaussian radial basis function (GRBF) is presented, so as to determine features that are sensitive to faults. Then, based on the selected features, an improved multiclass SVDD algorithm is developed to classify multiple classes of planetary gear faults, thus completing the fault diagnosis task. Finally, the effectiveness and advantage of the proposed method are demonstrated via experiments using wind turbine drivetrain diagnostics simulator (WTDDS), with comparison to several traditional methods.

This paper presents a fault diagnosis method called dominant trend based logistic regression (DTLR) for monitoring nonstationary processes. Different from conventional sample-wise diagnosis approaches, it uses sliding windows to collect process data and extract dominant trend features. After data preprocessing via robust sparse representation, the feature vector reflecting variation trend is obtained by solving a convex optimization problem, i.e., dominant trend extraction (DTE). Then the l 2 -norm of the dominant trend vector is used as a detection index to quantify the dissimilarity between normal and abnormal conditions. Once it exceeds the control limit, the feature vector is used to train the weight vector of logistic regression. The fault type can be determined as the class with the maximum conditional probability. With trend information, DTLR can effectively detect and isolate faults in nonstationary processes. Simulations on a synthetic nonstationary dynamic process, a nonstationary continuous stirred tank reactor (CSTR), and the real data of a blast furnace iron-making process illustrate superior monitoring and isolation performance of DTLR, compared with conventional methods.

For practical industrial processes, detection and isolation of a sensor precision degradation fault is of vital importance. In comparison with other sensor fault types such as complete failure and mean shift, the precision degradation fault is usually more difficult to detect and further to isolate. This paper proposes a new fault detection algorithm for sensor precision degradation using Kullback-Leibler divergence (KLD). The KLD is employed to quantify the dissimilarity between probability densities of each reference score and the actual one within the principal component analysis (PCA) framework. Under the assumption of Gaussian-distributed data, KLD for each score reduces to a measure of the variance difference, which is proven effective in detecting sensor precision degradation. Control limit of the KLD is established based on historical data, and online fault detection is implemented by adopting a sliding window. The fault isolation issue is also discussed, which aims to determine the faulty sensor with precision degradation. This task may be achieved by checking the PCA loading matrix and matching the fault detection result. Case studies on a synthetic numerical example and the continuous stirred tank reactor (CSTR) process are carried out to demonstrate the effectiveness of the proposed method, in comparison with conventional approaches. This article is protected by copyright. All rights reserved

In modern industry, detecting incipient faults timely is of vital importance to prevent serious system performance deterioration and ensure optimal process operation. Recently, multivariate statistical process monitoring (MSPM) techniques have been extensively studied and widely applied to modern industrial systems. However, conventional fault detection indices utilized in statistical process monitoring are not sensitive to incipient faults with small magnitude. In this paper, by introducing two representative smoothing techniques, novel incipient fault detection strategies based on a generic fault detection index in MSPM are proposed. Fault detectability for each proposed strategy is analyzed. In addition, the effects of the smoothing parameters on fault detection, including advantages and disadvantages, are also investigated. Finally, case studies on a numerical example and two practical industrial processes are carried out to demonstrate the effectiveness of the proposed incipient fault detection strategies.

Substation equipments are faced with incomplete information, multiple system data cannot be effectively shared, and maintenance personnel are not aware of the status of the equipment. In view of the above problems, this paper proposes a substation intelligent monitoring decision platform based on panorama data, which can realize the centralized access of substation main equipment and auxiliary equipment datas. The equipment state assessment model and intelligent load transfer decision-making base are established based on the decision platform, which realize the intelligent judgment of quantifying equipment fault and abnormal state by comprehensive evaluation method, and provide transfer strategy for substation based on device status discrimination result. Finally, The transfer strategy and model are validated by an actual engineering data, the results show that based on the method proposed in this paper, all optional load transfer schemes are formed and the best solution is given, which can improve the emergency repair and pre-control ability and have great practical value in the automation system of substation and deserves extensive application.

Introduction To introduce a classification for medial wall fragments in pertrochanteric femur fractures and investigate potential preoperative predictors of implant failure following fixation. Material and methods Medical records of 324 adult patients receiving routine operative treatment using intramedullary devices for pertrochanteric femur fractures with medial wall fragments between August 2008 and May 2018 were retrospectively analyzed. Potential predictors including age, gender, body mass index, comorbidities, AO/OTA classification of fractures were noted. The medial wall fractures were categorized into three types: 1) Type I: avulsion of the lesser trochanter; fracture line does not exceed the base of the lesser trochanter; 2) Type II: fragment involving the posterior cortex near the base of the lesser trochanter; fracture line does not reach the midline of the posterior wall; 3) Type III: fragment involving the large posterior cortex; fracture line reaches or exceeds the midline of the posterior wall. Results The 8 (2.5%) implant failures comprised 1 in 186 Type I fractures, 1 in 76 Type II fractures and 6 in 62 Type III fractures. The failure rates of each fracture type were 0.5% in Type I, 1.3% in Type II and significantly increased to 9.7% in Type III (odds ratio [OR], 19.821; 95% confidence interval [CI], 2.337-168.135; p=0.001). Conclusions Type III fractures had a significantly increased failure rate. It is important for orthopedists to identify Type III fractures presurgically, reduction of the medial wall fragment and fixation should be considered during surgery using intramedullary nails.

Background The incidence of intertrochanteric hip fracture is expected to increase as the global population ages. It is one of the most important causes of mortality and morbidities in the geriatric population. The incidence of reverse oblique and transverse intertrochanteric (AO/OTA 31-A3) fractures is relatively low; however, the incidence of implant failure in AO/OTA 31-A3 fractures is relatively high compared with that in AO/OTA 31-A1 and A2 fractures. To date, the risk factors for implant failure in AO/OTA 31-A3 fractures treated with proximal femoral nail antirotation (PFNA) have remained ambiguous. The purpose of this study was to identify the predictive factors of implant failure in AO/OTA 31-A3 fractures treated with PFNA. Methods The data of all patients who underwent surgery for trochanteric fractures at our institution between January 2006 and February 2018 were retrospectively reviewed. All AO/OTA 31-A3 fractures treated with PFNA were included. Logistic regression analysis of potential predictors of implant failure was performed. Potential predictors included age, sex, body mass index, fracture type, reduction method, status of posteromedial support and lateral femoral wall, reduction quality, tip-apex distance and position of the helical blade in the femoral head. Results One hundred four (9.3%) patients with AO/OTA 31-A3 fractures were identified. Forty-five patients with AO/OTA 31-A3 fractures treated with PFNA were suitable for our study. Overall, implant failure occurred in six (13.3%) of forty-five patients. Multivariate analysis identified poor reduction quality (OR, 28.70; 95% CI, 1.91–431.88; p = 0.015) and loss of posteromedial support (OR, 18.98; 95% CI, 1.40–257.08; p = 0.027) as factors associated with implant failure. Conclusions Poor reduction quality and loss of posteromedial support are predictors of implant failure in reverse oblique and transverse intertrochanteric fractures treated with PFNA.

In order to meet the requirements of precise power allocation for stable economic operation of microgrid systems, a distributed collaborative control strategy based on hierarchical microgrid is proposed in this paper. Considering the droop stability control of the parallel inverters as the first-level basic control layer, the second-level distributed voltage correction control strategy is designed to overcome the first-order voltage offset problem. At the same time, the power generation cost of the micro-source is selected as the objective function and the power matching of the micro-sources and load-demand are regarded as the constraints to realize a three-level consistent distributed optimization control strategy. Adopting the above three level control strategy, the output voltage fluctuation of microgrid system is reduced, the robustness of the system is enhanced and the optimal power distribution of the microgrid system is realized. Finally, a simulation model is built to verify the effectiveness of the proposed control strategy in stability and economic operation of the microgrid.

Reconstruction-based contribution (RBC) is widely used for fault isolation and estimation in conjunction with principal component analysis (PCA)-based fault detection. Correct isolation can be guaranteed by RBC for single-sensor faults with large magnitudes. However, the incipient sensor fault diagnosis problem is not well handled by traditional PCA and RBC methods. In this paper, the limitations of traditional PCA and RBC methods for incipient sensor fault diagnosis are illustrated and analyzed. Through the introduction of a moving window, a new strategy based on the PCA model is presented for incipient fault detection. Regarding incipient fault isolation and estimation, a new contribution analysis method called moving window RBC is proposed to enhance the isolation performance and estimation accuracy. Rigorous fault detectability and isolability analyses of the proposed methods are provided. In addition, effects of the window width on fault detection, isolation, and estimation are discussed. Simulation ...

For modern industrial processes, many sensors equipped operate in harsh environments and the large number of sensors increases the probability of sensor malfunction. In order to guarantee an optimal and efficient operating condition, incipient sensor fault detection and diagnosis become necessary and important. In the present work, a new data-driven process monitoring method called statistics Mahalanobis distance (SMD) is proposed for incipient fault detection of three common sensor fault types. Detectability analysis of SMD is provided and compared theoretically with the conventional approach. Besides, the effects of parameter selection in SMD on its fault detectability is briefly discussed. Then, a hierarchical strategy is proposed for subsequent fault diagnosis, including the fault isolation and fault classification aspects. Simulation studies on a numerical example and a benchmark process are carried out, which demonstrate the effectiveness and merits of the SMD based fault detection and diagnosis methods, in comparison with conventional approaches.

Reliability of the high-speed train air brake system is very critical to ensure a safe and comfortable operation environment for passengers. In our recently published work, a new monitoring strategy based on the concept of inter-variable variance (IVV) was proposed. It was illustrated by theoretical analysis and extensive experiments that this strategy is effective for several kinds of faults, and superior to the KNORR logic which is adopted by most high-speed trains in practice. However, the aforementioned strategy based on conventional IVV still suffers from two drawbacks. More specifically, due to the specific property of IVV, this strategy is unable to detect certain faults even if the fault is very serious; in addition, the detectability of conventional IVV for incipient faults with relatively small magnitudes still needs to improve. In this paper, a fault detection method incorporating the idea of four stages partition and involving a new combined statistic is presented to perform fault detection for high-speed train air brake systems. Through theoretical reasoning, it is pointed out that the proposed method can overcome drawbacks of the conventional IVV method to a large extent. To demonstrate the effectiveness and merits of the proposed fault detection method, in comparison with existing methods, experimental studies are carried out on a practical high-speed train air brake test bench.

Fisher discriminant analysis (FDA) is a widely used tool for fault diagnosis. In addition, many modifications have also been proposed recently in the literature in order to overcome certain limitations of the traditional FDA method. However, the incipient fault diagnosis problem is not well handled by traditional FDA and its variants. In this paper, through the introduction of smoothing techniques, a new method called smoothed FDA (SFDA) is proposed to enhance the fault diagnosis performance for incipient faults. Fault diagnosability analyses of the FDA and SFDA approaches are carried out and compared with each other. It is pointed out through theoretical analysis that SFDA is superior to FDA in terms of incipient fault diagnosis. Simulation studies on a continuous stirred tank reactor process are used to demonstrate the effectiveness of the SFDA method, in comparison with traditional FDA.

In modern process industries, many parameters or states can be acquired with sensors, and these parameters or states often have a close relationship with operation conditions. Unfortunately, the process often operates under different modes, and labels thereof are often unknown. In practice, labeling for sampled data is expensive and time-consuming, so identifying the operation conditions of the industrial process is difficult. In addition, sampled data from the industrial system are always contaminated by outliers or noise. Therefore, a robust process monitoring method for the multimode process is particularly important and challenging. In this paper, a robust dictionary learning method is proposed for processes with multiple unknown modes. Firstly, by taking the sparsity of outliers into account, a robust dictionary learning method is proposed to identify and remove the outliers and noise in the sampled training data. Secondly, an iterative minimization algorithm is designed for solving the dictionary learning optimization program. Thirdly, based on the sparsity of the sparse code, we partition the sparse code into different clusters via spectral clustering method, and then the dictionary is divided into some sub-dictionaries according to the cluster results of sparse code. Lastly, when a new sample is generated, we reconstruct it under different sub-dictionaries, and the smallest dictionary reconstruction error is calculated as a classifier for process monitoring and fault detection. To evaluate the validity and effectiveness of the proposed monitoring approach, we conduct extensive experiments on a numerical simulation, the continuous stirred tank heater (CSTH) process, and an industrial aluminum electrolysis process, in comparison with several state-of-the-art methods. The experimental results demonstrate that the proposed method is able to provide satisfying monitoring results, and it is also robust to outliers in the sampled training data. It is worth mentioning that the proposed method is an unsupervised learning method, therefore, it is more suitable for the process monitoring of real industrial systems. Accepted version

For modern industrial processes, timely detection of incipient faults is of vital importance so as to ensure safe and optimal process operation. Though recently statistical process monitoring (SPM) has been extensively studied and widely applied in practice, conventional multivariate statistics are usually not sensitive to incipient faults. In this paper, a new multivariate statistical index called augmented Mahalanobis distance (AMD) is proposed for incipient fault detection. It can be concluded from fault detectability analysis that the AMD index is more sensitive to incipient faults than the conventional Mahalanobis distance (MD) index. Besides, the idea of augmentation utilized in the AMD index can also be applied to some other SPM models. Finally, case studies on a numerical example and the continuous stirred tank heater (CSTH) process are conducted to demonstrate the effectiveness of the proposed AMD index, in comparison with the MD index, as well as the squared prediction error (SPE) and T-square indices.

The need exists for perioperative blood management measures aimed at improving patient outcomes and reducing the risks of allogeneic blood transfusion (ABT). Our study aim is to discuss an algorithm to predict the need for perioperative blood transfusion in old patients with pertrochanteric femoral fractures.We retrospectively analysed the data from 220 elderly patients with pertrochanteric femoral fractures with regard to the probability of receiving an ABT within 72h after surgery. The patients were divided into ABT and non-ABT groups. A univariate analysis was used to compare between-group differences with regard to 13 variables. A logistic regression analysis and a probability algorithm to predict the need for an ABT based on independent predictors were used.The non-ABT group included 131 patients (55 males and 76 females), with an average age of 77.2±6.8 years; the ABT group included 89 patients (29 males and 60 females), with an average age of 79.7±6.6 years. The total volume of transfused blood was 276 Units; the actual average blood transfusion was 3.1±1.47 Units. Significant between-group differences (P0.05) were observed with regard to age, duration of operation, haemoglobin (Hb) at admission, intra-operative blood loss, type of fracture and type of anaesthesia. The mean volume of transfused blood in the proximal femoral nail anti-rotation (PFNA) and Gamma3 group was larger than that of the dynamic hip screw (DHS) group (P0.05). A logistic regression analysis revealed that patients with pertrochanteric femoral fractures who were elderly (81 years), had lower Hb levels at admission (≤124g/L), longer duration of operations (t85min), underwent intramedullary fixation (Gamma3 and PFNA) and had more intra-operative blood loss were more likely to need an ABT. This regression model predicted 74.1% of the transfused cases.An algorithm was devised to predict and manage the need for an ABT within 72h after surgery in patients with pertrochanteric femoral fractures. A reasonable transfusion program might reduce the complications caused by anaemia and effectively avoid the risks associated with ABTs.

In process industries, it is necessary to conduct fault diagnosis after abnormality is found, with the aim to identify root cause variables and further provide instructive information for maintenance. Contribution plots along with multivariate statistical process monitoring are standard tools towards this goal, which, however, suffer from the smearing effect and high diagnostic complexity on large-scale processes. In fact, process variables tend to be naturally grouped, and in this work, a novel fault identification strategy based on group Lasso penalty along with a hierarchical fault diagnosis scheme is proposed by leveraging group information among variables. By introducing the group Lasso as a regularization approach, groups of irrelevant variables tend to yield exactly zero contributions collectively, which help find the exact root cause, alleviate the smearing effect, and furnish clear diagnostic information for process practitioners. For online computational convenience, an efficient numerical solution strategy is also presented. Besides, it turns out that the proposed approach also applies to dynamic monitoring models with lagged measurements augmented, thereby enjoying widespread generality. Its effectiveness is evaluated on both the Tennessee Eastman benchmark process and a pilot-scale experiment apparatus.

The air brake system is crucial to guarantee the safe operation of electric multiple unit (EMU) due to its high-speed characteristic. As an important device of the EMU air brake system, brake cylinder has a direct influence on the braking action. Brake cylinders are usually equipped with measurement sensors so as to monitor their pressures. Currently, univariate control chart is utilized to monitor each brake cylinder. That is, the brake cylinder is deemed as faulty if its pressure is beyond the permitted range. Sensor damage can also be easily detected by the detection circuit. However, the performance degradation of brake cylinder and soft sensor fault are not well handled by current monitoring strategies. In this paper, a new multivariate detection index called inter-variable variance (IVV) is proposed to monitor multiple brake cylinders simultaneously. The IVV is capable of detecting the performance degeneration of brake cylinder, as well as the soft sensor fault. Besides, these two types of fault can be distinguished from each other by making use of additional information. Experimental studies are carried out to demonstrate the effectiveness of the proposed fault detection strategy.

Kernel principal component analysis (KPCA) is a statistical analysis procedure that has been applied successfully for nonlinear process monitoring. However, it is still a challenging problem to determine the optimal kernel parameter during the KPCA modeling, which is very significant for both modeling and monitoring processes. In this paper, a new approach based on sample reconstruction is proposed to evaluate the KPCA modeling and determine the kernel parameter. By using artificial neural network (ANN), samples are reconstructed in the input space from the scores in the KPCA space. Then, an index named relative reconstruction error (RRE) is defined in order to search for the optimal parameter. A numerical case study is given to demonstrate the effectiveness of the proposed approach.

Background Although the modified tension band technique (eg, tension band supplemented by longitudinal Kirschner wires) has long been the mainstay for fixation of transverse fractures of the patella, it has shortcomings, such as bad reduction, loosening of implants, and skin irritation. Questions/purposes We conducted a retrospective comparison of the modified tension band technique and the titanium cable-cannulated screw tension band technique. Patients and Methods We retrospectively reviewed 101 patients aged 22 to 85 years (mean, 56.6 years) with AO/OTA 34-C1 fractures (n = 68) and 34-C2 fractures (n = 33). Fifty-two patients were in the modified tension band group and 49 were in the titanium cable-cannulated screw tension band group. Followup was at least 1 year (range, 1–3 years). Comparison criteria were fracture reduction, fracture healing time, and the Iowa score for knee function. Results The titanium cable-cannulated screw tension band group showed improved fracture reduction, reduced healing time, and better Iowa score, compared with the modified tension band group. In the modified tension band group, eight patients experienced wire migration, three of these requiring a second operation. There were no complications in the titanium cable-cannulated screw tension band group. Conclusions The titanium cable-cannulated screw tension band technique showed superior results and should be considered as an alternative method for treatment of transverse patellar fractures. Level of Evidence Level III, therapeutic study. See the Guidelines for Authors for a complete description of levels of evidence.

In resonant grounded system when single-phase earth fault occurs, for arc suppression coil cannot compensate the active current of the zero-sequence grounding current passing through the fault point, the zero-sequence active current passing through the fault line is the sum of those passing through the non-faults. Therefore in theory the zero-sequence active current passing through the fault line is greater than others in absolute value. Energy function is the integrating of product of zero-sequence voltage with zero-sequence current. The fault line's energy function value is negative, while the non-fault's is positive. If three-phase earth capacitance is imbalanced, zero-sequence voltage produces, which will produce zero-sequence current passing through all the line. This zero-sequence current is the key variable that influences the phase-angle between non-fault zero-sequence current and the zero-sequence voltage. This paper analyzes the influences in different compensation conditions in theory. Simulation results are presented to demonstrate that the validity of earth fault line detection based on energy function will be worse and worse accompanied with the increasing of earth resistance when single-phase earth fault occurs on the imbalanced phase, and indicate that energy function has its validity scope.

In order to keep the power factor at a high level and maintain acceptable voltage quality in distribution systems, a novel dynamic reactive compensation equipment is proposed. This equipment, which consists of a special regulating autotransformer with a vacuum on-load tap-changer and an intelligent power factor controller (IPFC), adopts step-voltage- regulating control method. It bases on the principle of the capacitor bank's output is in direct ratio with the square of the voltage works on it. In the course of tap- changer switching, the capacitor bank is not cut-out from the power system all along, it is proved in theory that dangerous over-voltage and associated inrush current will not occur. Thus security of the operation on capacitor bank is improved greatly and its natural life will increase much too. Compared with any other dynamic reactive compensation this novel equipment is less expensive and would be more likely to be put to be practical use. In the end, experimental results are given to verify that the proposed equipment has good characteristic of dynamic reactive compensation. (5 pages)